I have a question regarding the Java Memory Model (JMM) , particularly in the context of x86 architecture, which I find quite intriguing. One of the most confusing and often debated topics is the volatile modifier. I've heard a lot of misconceptions suggesting that volatile effectively forbids the use of cached values for fields marked with this modifier. Some even claim it prohibits the use of registers. However, as far as I understand, these are oversimplified notions. I've never encountered any instructions that explicitly forbid using caches or registers for storing such fields. I'm not even sure if such behavior is technically possible. So, my question is directed at experts in x86 architecture: What actually happens under the hood? What semantics does the volatile modifier guarantee? From what I've seen, it seems to implement a full memory barrier using the LOCK prefix combined with the add 0 instruction. Let's settle this debate once and for all. P.S. I'm really tired of hearing false claims from my fellow programmers about volatile . They keep repeating the same story about cache usage, and I strongly feel they are terribly mistaken! I have researched the Java Memory Model (JMM) and the use of the volatile modifier. I expected to find clear explanations on how volatile works in the context of x86 architecture, specifically regarding its impact on caching and register usage. However, I encountered conflicting information and misconceptions. I am seeking clarification from experts to understand the true semantics and behavior of volatile on x86 systems.

Answers (4)

Reputation: 235

Low-Level Implementation of `volatile`: Bytecode and Machine Instructions

This article represents the final piece of a broader exploration into the volatile modifier in Java. In Part 1, we examined the origins and semantics of volatile, providing a foundational understanding of its behavior. Part 2 focused on addressing misconceptions and delving into memory structures.

Now, in this conclusive installment, we will analyze the low-level implementation details, including machine-level instructions and processor-specific mechanisms, rounding out the complete picture of volatile in Java. Let’s dive in.

Exploring the Bytecode for `volatile` Fields

One common assumption among developers is that the volatile modifier in Java introduces specialized bytecode instructions to enforce its semantics. Let’s examine this hypothesis with a straightforward experiment.

Experimental Setup

I created a simple Java file named VolatileTest.java containing the following code:

public class VolatileTest {
    private volatile long someField;
}

Here, a single private field is declared as volatile. To investigate the bytecode, I compiled the file using the Java compiler (javac) from the Oracle OpenJDK JDK 1.8.0_431 (x86) distribution and then disassembled the resulting .class file with the javap utility, using the -v and -p flags for detailed output, including private members.

Comparing Results

I performed two compilations: one with the volatile modifier and one without it. Below are the relevant excerpts of the bytecode for the someField variable:

With volatile:

  private volatile long someField;
    descriptor: J
    flags: ACC_PRIVATE, ACC_VOLATILE

Without volatile:

  private long someField;
    descriptor: J
    flags: ACC_PRIVATE

The only difference is in the flags field. The volatile modifier adds the ACC_VOLATILE flag to the field’s metadata. No additional bytecode instructions are generated.

Hexadecimal Analysis

To explore further, I examined the compiled .class files using a hex editor (ImHex Hex Editor). The binary contents of the two files were nearly identical, differing only in the value of a single byte in the access_flags field, which encodes the modifiers for each field.

For the someField variable:

With volatile: 0x0042
Without volatile: 0x0002

The difference is due to the bitmask for ACC_VOLATILE, defined as 0x0040. This demonstrates that the presence of the volatile modifier merely toggles the appropriate flag in the access_flags field.

Modifiers and Flags

The access_flags field is a 16-bit value that encodes various field-level modifiers. Here’s a summary of relevant flags:

Modifier	Bit Value	Description
ACC_PUBLIC	`0x0001`	Field is `public`.
ACC_PRIVATE	`0x0002`	Field is `private`.
ACC_PROTECTED	`0x0004`	Field is `protected`.
ACC_STATIC	`0x0008`	Field is `static`.
ACC_FINAL	`0x0010`	Field is `final`.
ACC_VOLATILE	`0x0040`	Field is `volatile`.
ACC_TRANSIENT	`0x0080`	Field is `transient`.
ACC_SYNTHETIC	`0x1000`	Field is compiler-generated.
ACC_ENUM	`0x4000`	Field is part of an `enum`.

Implications

The volatile keyword’s presence in the bytecode is entirely represented by the ACC_VOLATILE flag. This flag is a single bit in the access_flags field. This minimal change emphasizes that there is no "magic" at the bytecode level—the entire behavior of volatile is represented by this single bit. The JVM uses this information to enforce the necessary semantics, without any additional complexity or hidden mechanisms.

x86 Processors and JVM Compatibility

Before diving into the low-level machine implementation of volatile, it is essential to understand which x86 processors this discussion pertains to and how these processors are compatible with the JVM.

Early JVM and x86 Support

When Java was first released, official support was limited to 32-bit architectures, as the JVM itself—known as the Classic VM from Sun Microsystems—was initially 32-bit. Early Java did not distinguish between editions like SE, EE, or ME; this differentiation began with Java 1.2. Consequently, the first supported x86 processors were those in the Intel 80386 family, as they were the earliest 32-bit processors in the architecture.

Intel 80386 processors, though already considered outdated at the time of Java's debut, were supported by operating systems that natively ran Java, such as Windows NT 3.51, Windows 95, and Solaris x86. These operating systems ensured compatibility with the x86 architecture and the early JVM.

Compatibility with Older x86 Processors

Interestingly, even processors as old as the Intel 8086, the first in the x86 family, could run certain versions of the JVM, albeit with significant limitations. This was made possible through the development of Java Platform, Micro Edition (Java ME), which offered a pared-down version of Java SE. Sun Microsystems developed a specialized virtual machine called K Virtual Machine (KVM) for these constrained environments. KVM required minimal resources, with some implementations running on devices with as little as 128 kilobytes of memory.

KVM's compatibility extended to both 16-bit and 32-bit processors, including those from the x86 family. According to the Oracle documentation in "J2ME Building Blocks for Mobile Devices," KVM was suitable for devices with minimal computational power:

"These devices typically contain 16- or 32-bit processors and a minimum total memory footprint of approximately 128 kilobytes."

Additionally, it was noted that KVM could work efficiently on CISC architectures such as x86:

"KVM is suitable for 16/32-bit RISC/CISC microprocessors with a total memory budget of no more than a few hundred kilobytes (potentially less than 128 kilobytes)."

Furthermore, KVM could run on native software stacks, such as RTOS (Real-Time Operating Systems), enabling dynamic and secure Java execution. For example:

"The actual role of a KVM in target devices can vary significantly. In some implementations, the KVM is used on top of an existing native software stack to give the device the ability to download and run dynamic, interactive, secure Java content on the device."

Alternatively, KVM could function as a standalone low-level system software layer:

"In other implementations, the KVM is used at a lower level to also implement the lower-level system software and applications of the device in the Java programming language."

This flexibility ensured that even early x86 processors, often embedded in devices with constrained resources, could leverage Java technologies. For instance, the Intel 80186 processor was widely used in embedded systems running RTOS and supported multitasking through software mechanisms like timer interrupts and cooperative multitasking.

Another example is the experimental implementation of the JVM for MS-DOS systems, such as the KaffePC Java VM. While this version of the JVM allowed for some level of Java execution, it excluded multithreading due to the strict single-tasking nature of MS-DOS. The absence of native multithreading in such environments highlights how certain Java features, including the guarantees provided by volatile, were often simplified, significantly modified, or omitted entirely. Despite this, as we shall see, the principles underlying volatile likely remained consistent with broader architectural concepts, ensuring applicability across diverse processor environments.

Implications for `volatile`

While volatile semantics were often simplified or omitted in these constrained environments, the core principles likely remained consistent with modern implementations. As our exploration will show, the fundamental ideas behind volatile behavior are deeply rooted in universal architectural concepts, making them applicable across diverse x86 processors.

Low-Level Solution to Reordering and Store Buffer Commit Issues

Finally, let’s delve into how volatile operations are implemented at the machine level. To illustrate this, we’ll examine a simple example where a volatile field is assigned a value. To simplify the experiment, we’ll declare the field as static (this does not influence the outcome).

public class VolatileTest {
    private static volatile long someField;

    public static void main(String[] args) {
        someField = 5;
    }
}

This code was executed with the following JVM options: -server -Xcomp -XX:+UnlockDiagnosticVMOptions -XX:+PrintAssembly -XX:CompileCommand=compileonly,VolatileTest.main

Experimental Setup and Execution Context

The test environment includes a dynamically linked hsdis library, enabling runtime disassembly of JIT-compiled code. The -Xcomp option forces the JVM to compile all code immediately, bypassing interpretation and allowing us to directly analyze the final machine instructions. The experiment was conducted on a 32-bit JDK 1.8, but identical results were observed across other versions and vendors of the HotSpot VM.

Here is the key assembly instruction generated for the putstatic operation targeting the volatile field:

0x026e3592: lock addl $0, (%esp)  ;*putstatic someField
                                  ; - VolatileTest::main@3 (line 5)

This instruction reveals the underlying mechanism for enforcing the volatile semantics during writes. Let’s dissect this line and understand its components.

Breaking Down the `LOCK` Prefix

The LOCK prefix plays a crucial role in ensuring atomicity and enforcing a memory barrier. However, since LOCK is a prefix and not an instruction by itself, it must be paired with another operation. Here, it is combined with the addl instruction, which performs an addition.

Why Use addl with LOCK?

Neutral Operation: The addl instruction adds 0 to the value at the memory address stored in %esp. Adding 0 ensures that the operation does not alter the memory's actual contents, making it a non-disruptive and lightweight operation.
Pointer to the Stack: %esp points to the top of the thread's stack, which is local to the thread and isolated from others. This ensures the operation is thread-safe and does not impact other threads or system-wide resources.
Minimal Overhead: The combination of LOCK with a no-op arithmetic operation introduces minimal performance overhead while triggering the required side effects.

Key Properties of the Stack and `%esp`

The %esp register (or %rsp in 64-bit systems) serves as the stack pointer, dynamically pointing to the top of the local execution stack. Since the stack is strictly local to each thread, its memory addresses are unique across threads, ensuring isolation.

The use of %esp in this context is particularly advantageous:

Isolation: Stack memory is inherently private to a thread, preventing cross-thread interference.
Dynamic Adaptation: The stack pointer updates automatically as the stack grows or shrinks, simplifying memory management.
Safety: The use of the stack pointer guarantees that the memory being "locked" is not shared, reducing contention risks.

Achieving the `volatile` Semantics

The LOCK prefix ensures:

Atomicity: No other thread or processor can access the specified memory region until the operation completes.
Memory Barrier Semantics: By default, the LOCK prefix enforces strong memory ordering guarantees, ensuring specific instruction sequences cannot be reordered across the barrier.

However, the mechanism does not enforce a complete draining of the store buffer in all cases. Only the stores that precede the barrier in program order (PO) are guaranteed to be committed to the coherent cache (L1d). This means the draining process is partial: it applies only to the stores that must be visible to subsequent operations as mandated by the memory model. Stores prepared for later commits (but not preceding the barrier) remain in the buffer until their turn in PO arrives.

This nuanced behavior explains why the LOCK prefix does not block all instructions. For example:

Loads and other lock-prefixed instructions* are delayed until older stores (relative to PO) are drained from the store buffer.
Independent instructions (such as computations or stores not requiring immediate commitment) can proceed without delay unless microarchitectural constraints dictate otherwise.

In summary, the LOCK prefix provides targeted control over memory ordering and visibility, ensuring:

Preceding writes are committed to the cache before subsequent loads.
Strict adherence to program order for store-store and store-load sequences.

This mechanism helps address issues related to reordering and store buffer visibility but operates selectively, without enforcing a complete halt on all subsequent operations.

A Note on Read Operations

Interestingly, no memory barrier is required for volatile reads on x86 architectures. The x86 memory model inherently prohibits Load-Load reorderings, which are the only type of reordering that volatile semantics would otherwise prevent for reads. Thus, the hardware guarantees are sufficient without additional instructions.

Atomicity of Writes and Reads in `volatile` Fields

Now, let us delve into the most intriguing aspect: ensuring atomicity for writes and reads of volatile fields. For 64-bit JVMs, this issue is less critical since operations, even on 64-bit types like long and double, are inherently atomic. Nonetheless, examining how write operations are typically implemented in machine instructions can provide deeper insights.

Code Example

For simplicity, consider the following code:

public class VolatileTest {
    private static volatile long someField;

    public static void main(String[] args) {
        someField = 10;
    }
}

Assembly Analysis of Write Operations

Here’s the generated machine code corresponding to the write operation:

0x0000019f2dc6efdb: movabsq       $0x76aea4020, %rsi
                                            ;   {oop(a 'java/lang/Class' = 'VolatileTest')}
0x0000019f2dc6efe5: movabsq       $0xa, %rdi
0x0000019f2dc6efef: movq          %rdi, 0x20(%rsp)
0x0000019f2dc6eff4: vmovsd        0x20(%rsp), %xmm0
0x0000019f2dc6effa: vmovsd        %xmm0, 0x68(%rsi)
0x0000019f2dc6efff: lock addl     $0, (%rsp)  ;*putstatic someField
                                            ; - VolatileTest::main@3 (line 5)

At first glance, the abundance of machine instructions directly interacting with registers might seem unnecessarily complex. However, this approach reflects specific architectural constraints and optimizations. Let us dissect these instructions step by step:

Detailed Breakdown of Instructions

movabsq $0x76aea4020, %rsi

This instruction loads the absolute address (interpreted as a 64-bit numerical value) into the general-purpose register %rsi. From the comment, we see this address points to the class metadata object (java/lang/Class) containing information about the class and its static members. Since our volatile field is static, its address is calculated relative to this metadata object.
- This approach ensures uniform handling of positive and negative 64-bit values due to the two's complement representation. While this may not seem critical here, it establishes a consistent method for managing both signed and unsigned integers.
movabsq $0xa, %rdi

Here, the immediate value 0xa (hexadecimal representation of 10) is loaded into the %rdi register. Since direct 64-bit memory writes using immediate values are prohibited in x86-64, this intermediate step is necessary.
movq %rdi, 0x20(%rsp)

The value from %rdi is then stored on the stack at an offset of 0x20 from the current stack pointer %rsp. This transfer is required because subsequent instructions will operate on SIMD registers, which cannot directly access general-purpose registers.
vmovsd 0x20(%rsp), %xmm0

This instruction moves the value from the stack into the SIMD register %xmm0. Although designed for floating-point operations, it efficiently handles 64-bit bitwise representations. The apparent redundancy here (loading and storing via the stack) is a trade-off for leveraging AVX optimizations, which can boost performance on modern microarchitectures like Sandy Bridge.
vmovsd %xmm0, 0x68(%rsi)

The value in %xmm0 is stored in memory at the address calculated relative to %rsi (0x68 offset). This represents the actual write operation to the volatile field.
lock addl $0, (%rsp)

The lock prefix ensures atomicity by locking the cache line corresponding to the specified memory address during execution. While addl $0 appears redundant, it serves as a lightweight no-op to enforce a full memory barrier, preventing reordering and ensuring visibility across threads.

Multiple Writes and Memory Barriers

Consider the following extended code:

public class VolatileTest {
    private static volatile long someField;

    public static void main(String[] args) {
        someField = 10;
        someField = 11;
        someField = 12;
    }
}

For this sequence, the compiler inserts a memory barrier after each write:

0x0000029ebe499bdb: movabsq       $0x76aea4070, %rsi
                                            ;   {oop(a 'java/lang/Class' = 'VolatileTest')}
0x0000029ebe499be5: movabsq       $0xa, %rdi
0x0000029ebe499bef: movq          %rdi, 0x20(%rsp)
0x0000029ebe499bf4: vmovsd        0x20(%rsp), %xmm0
0x0000029ebe499bfa: vmovsd        %xmm0, 0x68(%rsi)
0x0000029ebe499bff: lock addl     $0, (%rsp)  ;*putstatic someField
                                            ; - VolatileTest::main@3 (line 5)

0x0000029ebe499c04: movabsq       $0xb, %rdi
0x0000029ebe499c0e: movq          %rdi, 0x28(%rsp)
0x0000029ebe499c13: vmovsd        0x28(%rsp), %xmm0
0x0000029ebe499c19: vmovsd        %xmm0, 0x68(%rsi)
0x0000029ebe499c1e: lock addl     $0, (%rsp)  ;*putstatic someField
                                            ; - VolatileTest::main@9 (line 6)

0x0000029ebe499c23: movabsq       $0xc, %rdi
0x0000029ebe499c2d: movq          %rdi, 0x30(%rsp)
0x0000029ebe499c32: vmovsd        0x30(%rsp), %xmm0
0x0000029ebe499c38: vmovsd        %xmm0, 0x68(%rsi)
0x0000029ebe499c3d: lock addl     $0, (%rsp)  ;*putstatic someField
                                            ; - VolatileTest::main@15 (line 7)

Observations

The lock addl instruction follows each write, ensuring proper visibility and preventing reordering.
These barriers are mandatory and cannot be optimized away by the compiler due to the strict semantics of volatile.

In summary, the intricate sequence of operations underscores the JVM’s efforts to balance atomicity, performance, and compliance with the Java Memory Model.

For 32-Bit Systems: A Unique Challenge

When running the example code on a 32-bit JVM, the behavior differs significantly due to hardware constraints inherent to 32-bit architectures. Let’s dissect the observed assembly code:

0x02e837f0: movl        $0x2f62f848, %esi
                                        ;   {oop(a 'java/lang/Class' = 'VolatileTest')}
0x02e837f5: movl        $0xa, %edi
0x02e837fa: movl        $0, %ebx
0x02e837ff: movl        %edi, 0x10(%esp)
0x02e83803: movl        %ebx, 0x14(%esp)
0x02e83807: vmovsd      0x10(%esp), %xmm0
0x02e8380d: vmovsd      %xmm0, 0x58(%esi)
0x02e83812: lock addl   $0, (%esp)  ;*putstatic someField
                                        ; - VolatileTest::main@3 (line 5)

Register Constraints in 32-Bit Systems

Unlike their 64-bit counterparts, 32-bit general-purpose registers such as %esi and %edi lack the capacity to directly handle 64-bit values. As a result, long values in 32-bit environments are processed in two separate parts: the lower 32 bits ($0xa in this case) and the upper 32 bits ($0). Each part is loaded into a separate 32-bit register and later combined for further processing. This limitation inherently increases the complexity of ensuring atomic operations.

Atomicity Using SIMD Registers

Despite the constraints of 32-bit general-purpose registers, SIMD registers such as %xmm0 offer a workaround. The vmovsd instruction is used to load the full 64-bit value into %xmm0 atomically. The two halves of the long value, previously placed on the stack at offsets 0x10(%esp) and 0x14(%esp), are accessed as a unified 64-bit value during this operation. This highlights the JVM’s efficiency in leveraging modern instruction sets like AVX for compatibility and performance in older architectures.

For 32-Bit Systems: A More Intriguing Case

Let’s delve into the behavior of the same example but run on a 32-bit JVM. Below is the assembly output generated during execution:

0x02e837f0: movl        $0x2f62f848, %esi
                                    ;   {oop(a 'java/lang/Class' = 'VolatileTest')}
0x02e837f5: movl        $0xa, %edi
0x02e837fa: movl        $0, %ebx
0x02e837ff: movl        %edi, 0x10(%esp)
0x02e83803: movl        %ebx, 0x14(%esp)
0x02e83807: vmovsd      0x10(%esp), %xmm0
0x02e8380d: vmovsd      %xmm0, 0x58(%esi)
0x02e83812: lock addl   $0, (%esp)  ;*putstatic someField
                                    ; - VolatileTest::main@3 (line 5)

Here we see a similar unified approach to the 64-bit systems but driven more by necessity. In 32-bit systems, the absence of 64-bit general-purpose registers means the theoretical capabilities are significantly reduced.

Why Use `LOCK` Selectively?

In 32-bit systems, reads and writes are performed in two instructions rather than one. This inherently breaks atomicity, even with the LOCK prefix. While it might seem logical to rely on LOCK with its bus-locking capabilities, it is often avoided in such scenarios whenever possible due to its substantial performance impact.

To maintain a priority for non-blocking mechanisms, developers often rely on SIMD instructions, such as those involving XMM registers. In our example, the vmovsd instruction is used, which loads the values $0xa and $0 (representing the lower and upper 32-bit halves of the 64-bit long value) into two different 32-bit registers. These are then stored sequentially on the stack and combined atomically using vmovsd.

Simulating the Absence of AVX

What happens if the processor lacks AVX support? By disabling AVX explicitly (-XX:UseAVX=0), we simulate an environment without AVX functionality. The resulting changes in the assembly are:

0x02da3507: movsd       0x10(%esp), %xmm0 
0x02da350d: movsd       %xmm0, 0x58(%esi)

This highlights that the approach remains fundamentally the same. However, the vmovsd instruction is replaced with the older movsd from the SSE instruction set. While movsd lacks the performance enhancements of AVX and operates as a dual-operand instruction, it serves the same purpose effectively when AVX is unavailable.

When SSE is Unavailable

If SSE support is also disabled (-XX:UseSSE=0), the fallback mechanism relies on the Floating Point Unit (FPU):

0x02bc2449: fildll      0x10(%esp)
0x02bc244d: fistpll     0x58(%esi)

Here, the fildll and fistpll instructions load and store the value directly to and from the FPU stack, bypassing the need for SIMD registers. Unlike typical FPU operations involving 80-bit extended precision, these instructions ensure the value remains a raw 64-bit integer, avoiding unnecessary conversions.

The Challenge of Systems Without an FPU

For processors such as the Intel 80486SX or 80386 without integrated coprocessors, the situation becomes even more challenging. These processors lack native instructions like CMPXCHG8B (introduced in the Intel Pentium series) and 64-bit atomicity mechanisms. In such cases, ensuring atomicity requires software-based solutions, such as OS-level mutex locks, which are significantly heavier and less efficient.

Analyzing Reads from Volatile Fields

Finally, let’s examine the behavior during a read operation, such as when retrieving a value for display. The following assembly demonstrates the process:

0x02e62346: fildll      0x58(%ecx)
0x02e62349: fistpll     0x18(%esp)  ;*getstatic someField
                                    ; - VolatileTest::main@9 (line 7)

0x02e6234d: movl        0x18(%esp), %edi
0x02e62351: movl        0x1c(%esp), %ecx
0x02e62355: movl        %edi, (%esp)
0x02e62358: movl        %ecx, 4(%esp)
0x02e6235c: movl        %esi, %ecx  ;*invokevirtual println
                                    ; - VolatileTest::main@12 (line 7)

The read operation essentially mirrors the write process but in reverse. The value is loaded from memory (e.g., 0x58(%ecx)) into ST0, then safely written to the stack. Since the stack is inherently thread-local, this intermediate step ensures that any further operations on the value are thread-safe.

Test Environment Details

All experiments and observations in this article were conducted using the following hardware and software configuration:

Operating System

Edition: Windows 10 Enterprise
Version: 22H2
OS Build: 19045.5198

Processor

Model: Intel® Core™ i7-2960XM CPU @ 2.70 GHz
Architecture: x86-64

Java Development Kit (JDK)

Two versions of Oracle JDK 1.8.0_431 were used during the experiments:

64-bit JDK:
- Java HotSpot™ 64-Bit Server VM (build 25.431-b10, mixed mode).
32-bit JDK:
- Java HotSpot™ Client VM (build 25.431-b10, mixed mode, sharing).

JVM Settings

The following JVM options were applied:

-server -Xcomp -XX:+UnlockDiagnosticVMOptions -XX:+PrintAssembly -XX:CompileCommand=compileonly,VolatileTest.main

Tools

hsdis library: A dynamically linked disassembly library for analyzing machine code generated by the JIT compiler. The assembly code presented in this article follows the AT&T syntax, commonly used in Unix-like environments.
javap utility: Used for bytecode analysis with the -v -p flags.

Conclusion and Acknowledgment

This comprehensive exploration highlights the JVM's remarkable adaptability in enforcing volatile semantics across a range of architectures and processor capabilities. From AVX and SSE to FPU-based fallbacks, each approach balances performance, hardware limitations, and atomicity.

Thank you for accompanying me on this deep dive into volatile. This analysis has answered many questions and broadened my understanding of low-level JVM implementations. I hope it has been equally insightful for you!

Upvotes: 0

Dmytro Kostenko

Reputation: 235

Understanding Misconceptions About `volatile` in Java: A Comprehensive Analysis

Building on our initial discussion in Part 1, we now transition from high-level semantics to a detailed exploration of the low-level implementation of the volatile keyword in Java. This section aims to clarify its role in memory interactions, busting common misconceptions along the way.

Memory Hierarchy: The Building Blocks

To understand the low-level behavior of volatile, it’s crucial to examine the structure and function of computer memory. Broadly, memory can be categorized into three key areas:

Processor Registers
Processor Cache
Main Memory (RAM)

Let’s explore each of these components and their interactions in greater detail.

Processor Registers: Local and Ultra-Fast Memory

Registers are the fastest memory locations within a CPU, directly embedded in the processing cores and inherently local to the executing processor or core. Their size and configuration are tied to the processor's architecture. For instance, x86-64 processors typically feature 16 general-purpose registers, each capable of storing 64 bits.

Registers play a pivotal role in modern systems, acting as the first point of contact for intermediate calculations and frequently used operands. By ensuring repeated access within registers, such as in iterative operations or loops, they dramatically reduce latency compared to cache or main memory interactions.

Modern processors utilize registers extensively during out-of-order execution. This allows simultaneous processing of instructions by reordering their execution based on operand availability and dependencies. Temporary results are held in registers, ensuring rapid access during speculative execution and improving overall throughput.

Processor Cache: Bridging Speed Gaps Between CPU and Memory

Processor caches mitigate the speed disparity between the CPU's high-frequency operation and the comparatively slower main memory. Modern cache hierarchies rely on static random-access memory (SRAM), offering low latency and high-speed data access. Early processor designs omitted caches, as memory and bus speeds were sufficiently aligned. However, the gap widened in the 1990s, necessitating integrated caches to maintain performance.

Modern Organization: Modern CPUs employ multi-level caches:
- L1 Cache: Local to each core; fastest but smallest, often split into instruction (L1i) and data (L1d) caches.
- L2 Cache: Larger than L1, still local to cores, serving as a secondary buffer.
- L3 Cache: Shared across all cores in most designs. It is often referred to as the last-level cache (LLC) unless a higher cache level, such as L4, exists in the architecture. In such cases, L4 assumes the role of LLC.

Cache Lines and Data Locality

Caches operate with cache lines, typically 64 bytes on x86 processors. Accessing one memory address often fetches adjacent addresses into the cache, optimizing for spatial locality. For example, sequential memory operations, such as iterating over arrays, benefit significantly as related data is preloaded into faster-access memory.

Cache Coherence Protocols and L4 Experimentation

Caches must stay synchronized across cores to prevent stale data. Protocols like MESI (Modified, Exclusive, Shared, Invalid) ensure consistency across multi-threaded environments by defining the state of cached data:

Modified (M): Data in the cache has been updated but not written to main memory.
Exclusive (E): Data is consistent with main memory but not shared with other caches.
Shared (S): Data exists in multiple caches and matches main memory.
Invalid (I): The cache entry is outdated due to modifications elsewhere.

These protocols prevent stale reads and write conflicts. Modern processors, however, often rely on enhanced protocols like MESIF and MOESI, which add additional states (e.g., Forward and Owned) to optimize cache coherence, especially in multi-core and distributed systems.

Additionally, higher cache levels, such as L4 caches, have been experimentally introduced in some architectures. These typically use embedded DRAM (eDRAM) to act as an extended last-level cache (LLC), such as Intel's "Crystal Well" processors. Importantly, the LLC is not always the L3 cache; in systems with L4, it supersedes L3 as the LLC. A notable example includes Intel Xeon designs, where L3 is divided among clusters, and L4 becomes the system-wide shared cache. These protocols prevent stale reads and write conflicts, integral to multi-threaded operations involving volatile variables.

Main Memory (RAM): The Shared and Slowest Layer

Main memory, or Random Access Memory (RAM), serves as the largest and most shared memory layer across threads and processes. Its primary advantage is size, accommodating vast datasets, but this comes at the cost of higher latencies due to its reliance on Dynamic RAM (DRAM) technology. Accessing RAM incurs delays spanning hundreds of CPU cycles, emphasizing the importance of caches to mitigate performance bottlenecks.

Accessing RAM

Cache Misses: When data isn’t found in any cache level, it must be fetched from RAM, incurring significant latency.
Evictions: Overflowing cache lines result in data being written back to RAM, governed by replacement policies like LRU (Least Recently Used).

Direct memory access (DMA) and memory-mapped I/O can also influence how main memory is used in certain systems, but these typically operate outside the direct purview of application-level multithreading.

Debunking the Myths About `volatile`

Myth 1: `volatile` disables caching or forces direct access to RAM

One of the most prevalent misconceptions about the volatile modifier is the belief that it disables the CPU cache or forces the processor to bypass it entirely and work directly with main memory. Let’s clarify this misunderstanding in detail.

Even though early processors like the Intel 80486 allowed disabling caching by setting the Cache Disable (CD) bit in the CR0 register—effectively turning off all levels of caching—this behavior has nothing to do with the way volatile operates. Moreover, while x86 instruction sets include commands like WBINVD (Write Back and Invalidate Cache), INVD (Invalidate Cache), and CLFLUSH (Cache Line Flush), these are primarily low-level mechanisms intended for specialized use cases. Modern processors and their automated cache-coherence protocols (e.g., MESI) handle caching tasks much more efficiently without manual intervention.

Historical Context and Cache Control

Commands like WBINVD and INVD first appeared with the Intel 80486 processors, before MESI became a standard feature in later processor generations. Before the advent of MESI, programmers had to manually manage cache invalidations and flushes to ensure data consistency. However, modern processors manage cache coherence dynamically, rendering manual manipulation not only unnecessary but potentially counterproductive.

Why the Misconception Persists

The confusion likely stems from misunderstandings about modern memory hierarchies, especially the role of store buffers introduced with Intel’s P6 architecture (e.g., Pentium Pro). With out-of-order execution, processors gained the ability to reorder memory operations for improved performance.

For example, a write operation might temporarily remain in the store buffer, awaiting finalization. While the data resides there, it hasn’t yet been committed to the L1 cache or made visible to other processor cores. Automated coherence mechanisms only function once the data is fully committed, and until then, it remains effectively invisible to other threads. This behavior can lead to the perception that volatile bypasses the cache, but in reality, it works within these mechanisms to ensure consistency.

How `volatile` Influences This

The volatile modifier ensures that writes to a variable are committed promptly and that subsequent reads reflect the latest committed value. However, volatile does not disable the cache; instead, it uses memory barriers to enforce ordering and ensure that data written to the store buffer is visible across threads. This process interacts with the cache like any other operation but with added synchronization guarantees.

By leveraging modern CPU designs and Java’s Memory Model, volatile achieves consistent visibility without requiring manual manipulation of caches or bypassing their functionality.

Myth 2: `volatile` Limits the Use of Registers

A common claim in online discussions suggests that while volatile does not directly disable the CPU cache, it imposes restrictions on the use of registers. Let's unpack this assertion.

Some proponents argue that benchmarks show slight performance drops when using volatile, leading them to believe that registers are either underutilized or bypassed entirely. The claim often states that without volatile, registers can act as high-speed storage for computations involving shared memory, but once volatile is applied, data is immediately flushed back to memory, bypassing registers.

This idea, however, is fundamentally flawed. Registers are an essential component of processor architecture, used for storing operands and intermediate results. The idea of avoiding or "clearing" registers during volatile operations conflicts with how processors function. Results of calculations may still reside in registers even after a volatile write; the memory barriers introduced by volatile ensure timely data visibility without disrupting typical register usage.

Disassembly of code involving volatile demonstrates no significant difference in register usage compared to non-volatile code. Registers continue to be used actively, highlighting that volatile does not introduce unnecessary register-related overhead. Instead, the performance impact is due to memory barriers forcing writes to propagate from store buffers to cache and ensuring consistent visibility across threads.

In summary, volatile does not restrict the use of registers, and observed performance differences are attributable to synchronization mechanisms rather than register behavior.

Myth 3: `volatile` Is Unnecessary in Single-Processor Systems

It might seem logical to assume that volatile is irrelevant in single-processor systems since all threads share the same cache. However, there are several nuances to consider.

Hyper-Threading and Store-to-Load Forwarding

Take the legendary Intel Pentium 4 as an example. With the advent of Hyper-Threading (HT), a single physical core could host multiple hardware threads. In such setups, the store buffer—a temporary area for uncommitted writes—is split between threads. This means each hardware thread interacts with its isolated store buffer region, even though they share the same L1 cache.

A feature called Store-to-Load Forwarding (STLF) allows a thread to access data directly from its store buffer, even if the value hasn’t been committed to cache. However, another thread may not see these uncommitted updates, leading to potential inconsistencies. volatile resolves this issue by ensuring proper commitment of writes, making the data visible to all threads.

Atomicity for Long and Double in 32-Bit Systems

In 32-bit systems, reading or writing to long and double types can result in non-atomic operations, potentially leading to "half-written" data. The volatile modifier ensures atomicity for these operations, even in such environments, safeguarding against such issues.

Memory Reordering and `happens-before` Guarantees

Even in single-processor systems, memory reordering can cause logical inconsistencies. For example, without volatile, a thread might observe an updated variable while other preceding updates remain unseen. The volatile keyword enforces happens-before guarantees, ensuring a consistent order of operations and preventing reorderings that could lead to unexpected results.

This section demonstrates that volatile serves critical purposes, even in seemingly straightforward single-processor setups. It addresses complexities like thread-local store buffers, atomicity concerns, and memory reordering.

Myth 4: Immediate Visibility of `volatile` Writes

One of the most pervasive misconceptions about volatile is the assumption that changes to a volatile field are immediately visible to all threads, as if they are written directly to main memory. This belief often stems from the first myth about bypassing caches. While the process is indeed fast, it is not instantaneous.

Cache Coherence and Commit Overheads

The efficiency of cache coherence protocols is remarkable but not without latency. When a value is committed from the store buffer, it can trigger cache invalidation across other cores if the same cache line exists in a Shared state. This forces the initiating thread to transition its cache line to Exclusive, requiring synchronization and potentially updating main memory.

This description applies primarily to the classical MESI protocol, which was implemented in earlier x86 processors (starting with Intel Pentium, P5 architecture). In these cases, synchronization with main memory was often necessary to ensure cache coherence when modified data could no longer be retained solely in one cache.

However, modern processors utilize more advanced cache coherence protocols, such as MESIF (used in Intel processors starting with Nehalem) and MOESI (used in AMD processors starting with Opteron). These protocols significantly reduce the need for main memory synchronization in scenarios involving modified data shared across multiple cores.

MESIF introduces the Forward (F) state, allowing a single core to act as the designated responder when multiple cores hold a cache line in the Shared state. This avoids unnecessary invalidations and eliminates redundant interactions with main memory for data that is already consistent across the caches.
MOESI extends MESI with the Owned (O) state, which enables modified data to reside in one cache while being simultaneously shared with other caches. The Owned state ensures that the core holding the data is responsible for responding to requests from other cores, bypassing the need for immediate write-back to main memory until the cache line is evicted.

In both MESIF and MOESI, these optimizations minimize the overhead associated with cache coherence. They also ensure that data can be efficiently shared between cores without triggering unnecessary invalidations or main memory updates.

It is also important to consider the role of Invalidation Queues in this process. When multiple invalidation requests are sent to a target core under high load, these requests are queued for sequential processing. If the queue becomes saturated, delays can occur as the system waits for the queue to clear. This means that the invalidation of a desired cache line may have to wait its turn in the queue. While these delays are typically short, they introduce non-zero latency, which can manifest unexpectedly in certain scenarios.

For frequent operations on volatile fields, while cache invalidations and transitions to Exclusive may still occur, the advanced features of MESIF and MOESI reduce the frequency and severity of these overheads. However, developers should remain aware of potential delays caused by Invalidation Queue saturation in high-throughput systems. These considerations highlight that while modern protocols improve performance significantly, achieving consistency across cores is still not instantaneous.

Reading From Cache

Importantly, even with volatile, caches remain in use for reads. After the coherence protocol completes, the latest value can be stored and accessed directly from the cache, ensuring consistency. The time required to invalidate other cache lines or update memory varies but is never instantaneous.

Practical Considerations

In high-throughput systems with intense volatile usage, the overhead becomes more pronounced. To avoid relying on immediate visibility, it’s crucial to verify changes explicitly using spin-wait loops or other mechanisms. This ensures that cache coherence protocols and happens-before guarantees have fully established visibility for the updated value and preceding writes.

In summary, while volatile enforces visibility, its effects depend on hardware synchronization, which is fast but not instantaneous.

Conclusion

In this section, we tackled several widespread myths surrounding the volatile keyword in Java, uncovering its true role and dispelling misconceptions. From cache usage to register interactions and its relevance in single-processor systems, we explored the nuances that define its behavior under the hood.

In the next part, we will delve into specific machine-level instructions and their relationship with volatile, providing a deeper technical understanding of how it integrates with modern hardware and the Java Memory Model. Stay tuned!

Upvotes: 0

Dmytro Kostenko

Reputation: 235

A Comprehensive Dive Into `volatile` in Java: From Its Origins to Modern Semantics

Dear readers,
The Stack Overflow community has been instrumental in guiding me toward understanding the volatile modifier in Java. However, as I delved deeper into this topic, I realized the need to compile my thoughts into a comprehensive explanation that would benefit not only myself but also others seeking clarity on this intricate concept. This will be a multi-part exploration, as the depth of the subject demands it. While I might err in certain details, I aim to provide a detailed and accurate explanation. Let’s begin!

Part 1: `volatile` in Java — A Historical Perspective

Among all Java modifiers, volatile is perhaps the most challenging to fully understand. Unlike other modifiers, its guarantees have evolved significantly across different versions of the Java platform. To grasp its semantics, we must first delve into its origins and initial purpose.

The Origins of `volatile`

The volatile modifier has been part of Java since its very first release, reflecting the language’s ambition to dominate high-performance server-side applications. Multithreading support was fundamental to this vision, and Java borrowed heavily from languages like C++ to introduce essential synchronization primitives. volatile was one such primitive, designed to serve as a lightweight mechanism for managing visibility between threads.

Early Guarantees of `volatile`

In its initial iterations, volatile offered two main guarantees:

Visibility: Updates made to a volatile variable by one thread would eventually become visible to other threads. This ensures that no thread continues to operate indefinitely on stale data, as changes are reconciled with the main memory as quickly as feasible. Importantly, this does not imply immediate visibility but rather guarantees eventual consistency.
Atomicity of Simple Load and Store Operations:
For basic read and write operations, including those involving 64-bit types such as long and double, volatile ensured atomicity. However, compound operations (e.g., increment, decrement) remained non-atomic due to their multi-step nature (read-modify-write).

These guarantees are explicitly documented in the earliest edition of the Java Language Specification (JLS), which can still be accessed here. Let’s analyze two critical excerpts:

Visibility Guarantee

From Section 8.3.1.4 volatile Fields:

"Java provides a second mechanism that is more convenient for some purposes: a field may be declared volatile, in which case a thread must reconcile its working copy of the field with the master copy every time it accesses the variable. Moreover, operations on the master copies of one or more volatile variables on behalf of a thread are performed by the main memory in exactly the order that the thread requested."

This statement underscores that volatile ensures visibility by requiring threads to reconcile their working copies with the main memory whenever they interact with a volatile variable.

Atomicity for long and double

From Section 17.4 Nonatomic Treatment of double and long:

"If a double or long variable is not declared volatile, then for the purposes of load, store, read, and write actions they are treated as if they were two variables of 32 bits each."

In contrast, declaring a long or double as volatile ensures atomicity for basic load and store operations.

Challenges in Early Java Memory Models

Despite these guarantees, significant issues emerged due to the lack of a defined Java Memory Model (JMM). The specification imposed no strict constraints on memory interactions, leaving Java’s memory model largely undefined. As a result, Java's early memory model could be classified as weak, allowing for extensive reordering of read and write operations across different levels.

Sources of Reordering

Reordering could occur at three primary levels, each contributing to potential inconsistencies in multithreaded programs:

Bytecode Compiler (javac): While the bytecode compiler generally preserved the logical flow of a program, it occasionally performed minor optimizations. These optimizations might reorder operations in limited cases, although javac was relatively conservative compared to other sources of reordering. Its primary goal was to preserve the developer’s intent while generating efficient bytecode.
Just-In-Time (JIT) Compiler: The JIT compiler, introduced in JDK 1.1, became a significant source of reordering. Its purpose is to maximize runtime performance, often by aggressively optimizing code execution. Operating under the principle, “Anything not explicitly forbidden is permitted,” the JIT compiler dynamically analyzes code and reorders instructions to improve efficiency.

Several factors influence JIT behavior:
- JVM flags: Options such as -XX:+AggressiveOpts or tuning garbage collection can indirectly affect JIT optimizations.
- Workload: The JIT compiler may optimize code differently depending on runtime conditions, such as the frequency of method calls or contention on shared resources.
- Implementation: The behavior can vary across JIT compilers (e.g., C1 for client-side performance and C2 for server-side optimizations) and JVM versions. (In early versions of the HotSpot JVM, server vs. client VMs really did optimize differently. That hasn't been the case for years.)
This runtime adaptability makes it challenging to predict the exact sequence of operations in a multithreaded environment. Also, relying on a compiler not to do a specific optimization that's allowed on paper is not future-proof against later improvements in JIT compilers.
CPU-Level Reordering: Modern processors utilize instruction-level parallelism and sophisticated caching mechanisms to maximize throughput. These hardware-level optimizations often result in the reordering of independent instructions. For instance:
- Out-of-order execution allows the CPU to execute instructions based on the availability of execution units rather than their program order, allowing loads to run early. (Out-of-order completion of loads is also possible on in-order CPUs with non-blocking caches that do hit-under-miss and miss-under-miss, if they only stall when an instruction tries to read a load result that isn't ready yet. Memory reordering does still happen on CPUs that begin execution of each instruction in program order.)
- The store buffer decouples execution from cache-miss stores, and makes speculative execution of stores possible. It naturally introduces StoreLoad reordering, and out-of-order commit from it to L1d cache can introduce StoreStore reordering on ISAs which allow that.
- Memory subsystems, governed by protocols like MESI to maintain cache coherency, further complicate consistency by deferring visibility of updates across processor cores. But cache is coherent between cores that run threads of a Java program, and sequential consistency can be recovered with memory barriers that just control the order of a thread's own accesses to cache.
- See also How does memory reordering help processors and compilers?
These hardware behaviors add another layer of complexity to reasoning about multithreaded programs.

The interplay between these three levels—bytecode compiler, JIT compiler, and CPU—compounded the challenges of managing memory consistency in early Java.

A Classic Example of Reordering in Early Java

Consider the following program, which illustrates the pitfalls of weak memory models:

public class ReorderingExample { 
    private int x;
    private int y;

    public void T1() {
        x = 1;
        int r1 = y;
    }

    public void T2() {
        y = 1;
        int r2 = x;
    }
}

In this example:

The absence of volatile allows the bytecode compiler, JIT compiler, and CPU to freely reorder operations.
Possible outcomes for r1 and r2 include inconsistent states, such as (0, 0), (1, 0), or (0, 1).

To ensure consistent results in early Java, the only option was to declare both fields as volatile.

Why `volatile` Prevents Reordering

The JLS explicitly prohibits reordering operations involving volatile variables. From Section 8.3.1.4:

"Operations on the master copies of one or more volatile variables on behalf of a thread are performed by the main memory in exactly the order that the thread requested."

This rule applies to all volatile variables, even if they are distinct. Therefore, no read or write operation involving a volatile variable can be reordered with another.

The Role of JIT in Reordering

To better understand the challenges of reordering, we need to consider the dynamic nature of the JIT compiler:

Unlike javac, which operates statically, the JIT compiler performs runtime optimizations that vary based on system conditions and workload.
Its guiding principle, “Anything not explicitly forbidden is permitted,” leads to aggressive reordering and optimization.
Predicting JIT behavior is inherently difficult, as it depends on a combination of runtime factors such as thread contention, memory pressure, and JVM-specific implementation details.

A Partial Fix with `volatile`

Let’s revisit the earlier example with one field declared as volatile:

public class ReorderingExample { 
    private volatile int x; 
    private int y; 

    public void T1() {
        x = 1;
        int r1 = y;
    }

    public void T2() {
        y = 1;
        int r2 = x;
    }
}

Declaring x as volatile guarantees:

Updates to x will become visible to all threads as quickly as possible.
Writes to x cannot be reordered with subsequent reads or writes to x.

However, operations involving y remain unrestricted. To fully eliminate the possibility of reordering, both fields must be declared as volatile.

Part 2: The Introduction of the New Java Memory Model and Its Impact on `volatile`

A pivotal moment in the evolution of Java’s memory model occurred with the introduction of JSR-133: Java™ Memory Model and Thread Specification Revision. The finalization of JSR-133 coincided with the release of Java 2 Platform Standard Edition 5.0 (J2SE 5.0), codenamed Tiger. This release marked a significant milestone in multithreading capabilities, bringing numerous enhancements to the Java ecosystem.

Key Innovations in J2SE 5.0

The J2SE 5.0 release introduced several groundbreaking features:

The java.util.concurrent library: A suite of tools for parallel programming, developed under the guidance of Doug Lea, which significantly simplified multithreaded application development.
Optimized intrinsic locks: Enhancements to internal locking mechanisms introduced strategies such as Biased Locking and Lightweight Locking (Biased Locking has since been deprecated).
The New Java Memory Model (New JMM): A more robust and formalized memory model that replaced the loosely defined approach of earlier Java versions.

The New JMM addressed long-standing consistency issues and provided a solid foundation for multithreaded programming. While it impacted a variety of synchronization mechanisms, such as synchronized blocks and final fields, the focus here will be on its implications for volatile.

Changes to `volatile` in the New JMM

The New JMM preserved the original guarantees of volatile—visibility and atomicity for simple load and store operations. However, it introduced additional guarantees, most notably:

Reordering restrictions through the happens-before (hb) relationship: A formalization that defines memory consistency rules.
Clarifications on synchronization semantics: The introduction of release-acquire actions tied to volatile operations.

The Happens-Before Relationship and `volatile`

The happens-before relationship, introduced in Section 17.4.5 of the JLS, provides the theoretical framework for memory synchronization:

Two actions can be ordered by a happens-before relationship. If one action happens-before another, then the first is visible to and ordered before the second.

This framework ensures consistency in multithreaded interactions and underpins the new guarantees for volatile. Specifically:

A write to a volatile field happens-before every subsequent read of that field.
If hb(x, y) and hb(y, z), then hb(x, z).

This transitivity is critical for ensuring that changes made by one thread propagate correctly and predictably to other threads.

Practical Implications of the Happens-Before Relationship

Synchronization Mechanisms and Acquire/Release Semantics

The establishment of a happens-before relationship in a multithreaded environment requires the use of synchronization mechanisms with acquire/release semantics:

Release action: Writing to a volatile field.
Acquire action: Reading from the same volatile field.

These actions ensure proper ordering and visibility of changes. For instance:

Thread T1 writes to a volatile variable (release action).
Thread T2 reads from the same volatile variable (acquire action).

Once T2 observes the value written by T1, all prior changes made by T1 (before the volatile write) become visible to T2. However, this relationship is only established when the write operation is fully performed and becomes visible to the entire system.

Eventual Visibility and Propagation Delays

It is crucial to emphasize that happens-before relationships are established at runtime and depend on the propagation of changes across the system. This process is not instantaneous. While modern systems optimize for low latency, synchronization still takes measurable time due to:

Cache coherence protocols: Mechanisms like MESI (and its derivatives) govern the invalidation and propagation of cache lines.
Invalidation queues: When multiple cores attempt to synchronize concurrently, delays can occur if invalidation queues are overloaded.

For example:

When Thread T1 writes to a volatile field, the CPU core needs to get MESI Exclusive ownership of the cache line before it can modify its copy of that line in its L1d cache by committing the store from the store buffer. It will send out a Read-For-Ownership (RFO), or just an Invalidate if it already has a copy, and wait for a response. Otherwise we could end up with two cores with conflicting changes to the same cache line, exactly what MESI prevents.
Other cores must invalidate their copies of the cache line to maintain consistency. But they may send back an acknowledgement of the invalidate request before fully processing it, putting the request in their invalidation queue, so loads might continue to be able to read the old data for a little while longer than you might expect.
After invalidating their own copy of the line, loads by other threads will miss in cache. They'll have to get a copy by doing a MESI Share request; the writer will downgrade their cache line to Shared and send a copy to core that requested it. (Or write-back as far as a shared L3 cache so other cores can get it from there. Modifications to MESI like MESIF and MOESI in real CPUs let them avoid writing back all the way to DRAM when sharing recently-written "dirty" cache lines.)

This propagation process introduces a delay, albeit minimal in most scenarios. However, the visibility of changes depends on the successful propagation of the specific write operation being observed.

Programming Considerations for Volatile Synchronization

The eventual consistency of volatile operations necessitates careful programming practices:

Do not assume instantaneous updates: The propagation of changes between threads, while fast, is not immediate. Code relying on volatile must account for this delay.
Verify expected values explicitly: If a specific value is expected to be written by one thread and read by another, implement logic to check for that value (e.g., a spin-wait loop or condition check). For example:
```
while (!flag) {
    // Spin-wait until the flag is set to true by another thread
}
```
This ensures that the desired changes are observed before proceeding.
Avoid assumptions about timing: The exact moment when a happens-before relationship is established cannot be predicted. It depends on runtime factors, including system load and hardware behavior.

A load will either see a store from another thread or not. Whether that's due to inter-thread latency or just simple timing (the thread doing the store ran later) is normally irrelevant for correctness. Think about ordering, not timing.

Reordering Restrictions in the New JMM

The New JMM imposes strict constraints on reordering around volatile fields:

Writes Before a volatile Write: Operations preceding a volatile write cannot be reordered to occur after it.
Reads After a volatile Read: Operations following a volatile read cannot be reordered to occur before it.

Additionally:

Store-Store Reordering: Writes to volatile fields cannot be reordered with subsequent writes to non-volatile fields.
Load-Load Reordering: Reads from volatile fields cannot be reordered with subsequent reads from non-volatile fields.

These rules define memory barriers around volatile operations, ensuring predictable behavior and data consistency.

Establishing Happens-Before Relationships in Practice

The critical point for developers to understand is that a happens-before relationship is only established when the observed write operation is completed and visible to the reading thread. This visibility is a runtime phenomenon, not a compile-time guarantee. For instance:

Thread T2 cannot establish a happens-before relationship with a write from Thread T1 until it observes the exact value written by T1.
If multiple writes occur, the reading thread must confirm the presence of the specific write it expects.

This behavior underscores the dynamic nature of happens-before relationships and the importance of designing code to account for eventual consistency. By properly utilizing volatile and other synchronization primitives, developers can ensure correct and consistent behavior in multithreaded programs.

Conclusion

The New Java Memory Model resolved many of the consistency issues inherent in earlier versions of Java. By formalizing the happens-before relationship, it introduced a robust framework for managing multithreaded interactions. While this discussion focused on volatile and its updated semantics, the next step is to explore the hardware-level implementation of these guarantees and how they interact with modern CPU architectures.

Stay tuned for the next section, where we dive deeper into these implementation details!

Upvotes: 1

Solomon Slow

Reputation: 27190

Not really answering your question, because I'm not going to say anything about x86 architecture, but as rzwitserloot said, you really should not worry about the underlying architecture when you write Java code.

So anyway, according to the rules in the JLS, If you remove volatile from the declaration of v, then the assert in this program could fail. With volatile, it cannot fail.

class VTest {
    static int x = 0;
    static volatile int v = 0;

    public static void main(String[] args) {
        Thread t = new Thread(() -> {
            x = 7;
            v = 5;
        });
        t.start();
        int local_v = v;
        int local_x = x;
        if (local_v == 5) {
            assert local_x == 7;
    }
}

Making v volatile guarantees that everything the new thread did before it assigned v=5 will be visible to the main thread after the main thread reads v *IF* the main thread sees the 5 that the new thread wrote.

There is no guarantee that the main thread will see v==5, but if it does see v==5, then it must also see x==7.

Upvotes: 3

Understanding the volatile Modifier in the Context of x86 Architecture and the Java Memory Model (JMM)

Answers (4)

Low-Level Implementation of volatile: Bytecode and Machine Instructions

Exploring the Bytecode for volatile Fields

Experimental Setup

Comparing Results

Hexadecimal Analysis

Modifiers and Flags

Implications

x86 Processors and JVM Compatibility

Early JVM and x86 Support

Compatibility with Older x86 Processors

Implications for volatile

Low-Level Solution to Reordering and Store Buffer Commit Issues

Experimental Setup and Execution Context

Breaking Down the LOCK Prefix

Key Properties of the Stack and %esp

Achieving the volatile Semantics

A Note on Read Operations

Atomicity of Writes and Reads in volatile Fields

Code Example

Assembly Analysis of Write Operations

Detailed Breakdown of Instructions

Multiple Writes and Memory Barriers

Observations

For 32-Bit Systems: A Unique Challenge

Register Constraints in 32-Bit Systems

Atomicity Using SIMD Registers

For 32-Bit Systems: A More Intriguing Case

Why Use LOCK Selectively?

Simulating the Absence of AVX

When SSE is Unavailable

The Challenge of Systems Without an FPU

Analyzing Reads from Volatile Fields

Test Environment Details

Conclusion and Acknowledgment

Understanding Misconceptions About volatile in Java: A Comprehensive Analysis

Memory Hierarchy: The Building Blocks

Processor Registers: Local and Ultra-Fast Memory

Processor Cache: Bridging Speed Gaps Between CPU and Memory

Main Memory (RAM): The Shared and Slowest Layer

Debunking the Myths About volatile

Myth 1: volatile disables caching or forces direct access to RAM

Historical Context and Cache Control

Why the Misconception Persists

How volatile Influences This

Myth 2: volatile Limits the Use of Registers

Myth 3: volatile Is Unnecessary in Single-Processor Systems

Hyper-Threading and Store-to-Load Forwarding

Atomicity for Long and Double in 32-Bit Systems

Memory Reordering and happens-before Guarantees

Myth 4: Immediate Visibility of volatile Writes

Cache Coherence and Commit Overheads

Reading From Cache

Practical Considerations

Conclusion

A Comprehensive Dive Into volatile in Java: From Its Origins to Modern Semantics

Part 1: volatile in Java — A Historical Perspective

The Origins of volatile

Early Guarantees of volatile

Challenges in Early Java Memory Models

A Classic Example of Reordering in Early Java

Why volatile Prevents Reordering

The Role of JIT in Reordering

A Partial Fix with volatile

Part 2: The Introduction of the New Java Memory Model and Its Impact on volatile

Key Innovations in J2SE 5.0

Changes to volatile in the New JMM

The Happens-Before Relationship and volatile

Practical Implications of the Happens-Before Relationship

Programming Considerations for Volatile Synchronization

Reordering Restrictions in the New JMM

Establishing Happens-Before Relationships in Practice

Conclusion

Related Questions

Low-Level Implementation of `volatile`: Bytecode and Machine Instructions

Exploring the Bytecode for `volatile` Fields

Implications for `volatile`

Breaking Down the `LOCK` Prefix

Key Properties of the Stack and `%esp`

Achieving the `volatile` Semantics

Atomicity of Writes and Reads in `volatile` Fields

Why Use `LOCK` Selectively?

Understanding Misconceptions About `volatile` in Java: A Comprehensive Analysis

Debunking the Myths About `volatile`

Myth 1: `volatile` disables caching or forces direct access to RAM

How `volatile` Influences This

Myth 2: `volatile` Limits the Use of Registers

Myth 3: `volatile` Is Unnecessary in Single-Processor Systems

Memory Reordering and `happens-before` Guarantees

Myth 4: Immediate Visibility of `volatile` Writes

A Comprehensive Dive Into `volatile` in Java: From Its Origins to Modern Semantics

Part 1: `volatile` in Java — A Historical Perspective

The Origins of `volatile`

Early Guarantees of `volatile`

Why `volatile` Prevents Reordering

A Partial Fix with `volatile`

Part 2: The Introduction of the New Java Memory Model and Its Impact on `volatile`

Changes to `volatile` in the New JMM

The Happens-Before Relationship and `volatile`