CODEWITHSUNDEEP
c++cperformancecachinggcc
excray
excray

Reputation: 2858

Structure of arrays and array of structures - performance difference

I have a class like this:

//Array of Structures
class Unit
{
  public:
    float v;
    float u;
    //And similarly many other variables of float type, upto 10-12 of them.
    void update()
    {
       v+=u;
       v=v*i*t;
       //And many other equations
    }
};

I create an array of objects of Unit type. And call update on them.

int NUM_UNITS = 10000;
void ProcessUpdate()
{
  Unit *units = new Unit[NUM_UNITS];
  for(int i = 0; i < NUM_UNITS; i++)
  {
    units[i].update();
  }
}

In order to speed up things, and possibly autovectorize the loop, I converted AoS to structure of arrays.

//Structure of Arrays:
class Unit
{
  public:
  Unit(int NUM_UNITS)
  {
    v = new float[NUM_UNITS];
  }
  float *v;
  float *u;
  //Mnay other variables
  void update()
  {
    for(int i = 0; i < NUM_UNITS; i++)
    {
      v[i]+=u[i];
      //Many other equations
    }
  }
};

When the loop fails to autovectorize, i am getting a very bad performance for structure of arrays. For 50 units, SoA's update is slightly faster than AoS.But then from 100 units onwards, SoA is slower than AoS. At 300 units, SoA is almost twice as worse. At 100K units, SoA is 4x slower than AoS. While cache might be an issue for SoA, i didnt expect the performance difference to be this high. Profiling on cachegrind shows similar number of misses for both approach. Size of a Unit object is 48 bytes. L1 cache is 256K, L2 is 1MB and L3 is 8MB. What am i missing here? Is this really a cache issue?

Edit: I am using gcc 4.5.2. Compiler options are -o3 -msse4 -ftree-vectorize.

I did another experiment in SoA. Instead of dynamically allocating the arrays, i allocated "v" and "u" in compile time. When there are 100K units, this gives a performance which is 10x faster than the SoA with dynamically allocated arrays. Whats happening here? Why is there such a performance difference between static and dynamically allocated memory?

Upvotes: 13

Views: 8185

Answers (4)

user1593842
user1593842

Reputation: 357

Two things you should be aware that can made a huge difference, depending on your CPU:

  1. alignment
  2. cache line aliasing

Since you are using SSE4, using a specialized memory allocation function that returns an address that aligned at a 16 byte boundary instead of new may give you a boost, since you or the compiler will be able to use aligned load and stores. I have not noticed much difference in newer CPUs, but using unaligned load and stores on older CPUs may be a little bit slower.

As for cache line aliasing, Intel explicit mentions it on its reference manuals (search for "Intel® 64 and IA-32 Architectures Optimization Reference Manual"). Intel says it is something you should be aware, specially when using SoA. So, one thing you can try is to pad your arrays so the lower 6 bits of their addresses are different. The idea is to avoid having them fighting for the same cache line.

Upvotes: 1

tim18
tim18

Reputation: 620

Certainly, if you don't achieve vectorization, there's not much incentive to make an SoA transformation.

Besides the fairly wide de facto acceptance of __RESTRICT, gcc 4.9 has adopted #pragma GCC ivdep to break assumed aliasing dependencies.

As to use of explicit prefetch, if it is useful, of course you may need more of them with SoA. The primary point might be to accelerate DTLB miss resolution by fetching pages ahead, so your algorithm could become more cache hungry.

I don't think intelligent comments could be made about whatever you call "compile time" allocation without more details, including specifics about your OS. There's no doubt that the tradition of allocating at a high level and re-using the allocation is important.

Upvotes: 0

Max
Max

Reputation: 121

Prefetches are critical to code that spends most of its execution time waiting for data to show up. Modern front side busses have enough bandwidth that prefetches should be safe to do, provided that your program isn't going too far ahead of its current set of loads.

For various reasons, structures and classes can create numerous performance issues in C++, and may require more tweaking to get acceptable levels of performance. When code is large, use object-oriented programming. When data is large (and performance is important), don't.

float v[N];
float u[N];
    //And similarly many other variables of float type, up to 10-12 of them.
//Either using an inlined function or just adding this text in main()
       v[j] += u[j];
       v[j] = v[j] * i[j] * t[j];

Upvotes: 1

Sergey K.
Sergey K.

Reputation: 25396

Structure of arrays is not cache friendly in this case.

You use both u and v together, but in case of 2 different arrays for them they will not be loaded simultaneously into one cache line and cache misses will cost huge performance penalty.

_mm_prefetch can be used to make AoS representation even faster.

Upvotes: 10

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