c++ - Why are elementwise additions much faster in separate loops than in a combined loop?

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Top 5 Answer for c++ - Why are elementwise additions much faster in separate loops than in a combined loop?

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Upon further analysis of this, I believe this is (at least partially) caused by the data alignment of the four-pointers. This will cause some level of cache bank/way conflicts.

If I've guessed correctly on how you are allocating your arrays, they are likely to be aligned to the page line.

This means that all your accesses in each loop will fall on the same cache way. However, Intel processors have had 8-way L1 cache associativity for a while. But in reality, the performance isn't completely uniform. Accessing 4-ways is still slower than say 2-ways.

EDIT: It does in fact look like you are allocating all the arrays separately. Usually when such large allocations are requested, the allocator will request fresh pages from the OS. Therefore, there is a high chance that large allocations will appear at the same offset from a page-boundary.

Here's the test code:

int main(){     const int n = 100000;  #ifdef ALLOCATE_SEPERATE     double *a1 = (double*)malloc(n * sizeof(double));     double *b1 = (double*)malloc(n * sizeof(double));     double *c1 = (double*)malloc(n * sizeof(double));     double *d1 = (double*)malloc(n * sizeof(double)); #else     double *a1 = (double*)malloc(n * sizeof(double) * 4);     double *b1 = a1 + n;     double *c1 = b1 + n;     double *d1 = c1 + n; #endif      //  Zero the data to prevent any chance of denormals.     memset(a1,0,n * sizeof(double));     memset(b1,0,n * sizeof(double));     memset(c1,0,n * sizeof(double));     memset(d1,0,n * sizeof(double));      //  Print the addresses     cout << a1 << endl;     cout << b1 << endl;     cout << c1 << endl;     cout << d1 << endl;      clock_t start = clock();      int c = 0;     while (c++ < 10000){  #if ONE_LOOP         for(int j=0;j<n;j++){             a1[j] += b1[j];             c1[j] += d1[j];         } #else         for(int j=0;j<n;j++){             a1[j] += b1[j];         }         for(int j=0;j<n;j++){             c1[j] += d1[j];         } #endif      }      clock_t end = clock();     cout << "seconds = " << (double)(end - start) / CLOCKS_PER_SEC << endl;      system("pause");     return 0; } 

Benchmark Results:

EDIT: Results on an actual Core 2 architecture machine:

2 x Intel Xeon X5482 Harpertown @ 3.2 GHz:

#define ALLOCATE_SEPERATE #define ONE_LOOP 00600020 006D0020 007A0020 00870020 seconds = 6.206  #define ALLOCATE_SEPERATE //#define ONE_LOOP 005E0020 006B0020 00780020 00850020 seconds = 2.116  //#define ALLOCATE_SEPERATE #define ONE_LOOP 00570020 00633520 006F6A20 007B9F20 seconds = 1.894  //#define ALLOCATE_SEPERATE //#define ONE_LOOP 008C0020 00983520 00A46A20 00B09F20 seconds = 1.993 


  • 6.206 seconds with one loop and 2.116 seconds with two loops. This reproduces the OP's results exactly.

  • In the first two tests, the arrays are allocated separately. You'll notice that they all have the same alignment relative to the page.

  • In the second two tests, the arrays are packed together to break that alignment. Here you'll notice both loops are faster. Furthermore, the second (double) loop is now the slower one as you would normally expect.

As @Stephen Cannon points out in the comments, there is a very likely possibility that this alignment causes false aliasing in the load/store units or the cache. I Googled around for this and found that Intel actually has a hardware counter for partial address aliasing stalls:


5 Regions - Explanations

Region 1:

This one is easy. The dataset is so small that the performance is dominated by overhead like looping and branching.

Region 2:

Here, as the data sizes increase, the amount of relative overhead goes down and the performance "saturates". Here two loops is slower because it has twice as much loop and branching overhead.

I'm not sure exactly what's going on here... Alignment could still play an effect as Agner Fog mentions cache bank conflicts. (That link is about Sandy Bridge, but the idea should still be applicable to Core 2.)

Region 3:

At this point, the data no longer fits in the L1 cache. So performance is capped by the L1 <-> L2 cache bandwidth.

Region 4:

The performance drop in the single-loop is what we are observing. And as mentioned, this is due to the alignment which (most likely) causes false aliasing stalls in the processor load/store units.

However, in order for false aliasing to occur, there must be a large enough stride between the datasets. This is why you don't see this in region 3.

Region 5:

At this point, nothing fits in the cache. So you're bound by memory bandwidth.

2 x Intel X5482 Harpertown @ 3.2 GHz Intel Core i7 870 @ 2.8 GHz Intel Core i7 2600K @ 4.4 GHz

vote vote


OK, the right answer definitely has to do something with the CPU cache. But to use the cache argument can be quite difficult, especially without data.

There are many answers, that led to a lot of discussion, but let's face it: Cache issues can be very complex and are not one dimensional. They depend heavily on the size of the data, so my question was unfair: It turned out to be at a very interesting point in the cache graph.

@Mysticial's answer convinced a lot of people (including me), probably because it was the only one that seemed to rely on facts, but it was only one "data point" of the truth.

That's why I combined his test (using a continuous vs. separate allocation) and @James' Answer's advice.

The graphs below shows, that most of the answers and especially the majority of comments to the question and answers can be considered completely wrong or true depending on the exact scenario and parameters used.

Note that my initial question was at n = 100.000. This point (by accident) exhibits special behavior:

  1. It possesses the greatest discrepancy between the one and two loop'ed version (almost a factor of three)

  2. It is the only point, where one-loop (namely with continuous allocation) beats the two-loop version. (This made Mysticial's answer possible, at all.)

The result using initialized data:

Enter image description here

The result using uninitialized data (this is what Mysticial tested):

Enter image description here

And this is a hard-to-explain one: Initialized data, that is allocated once and reused for every following test case of different vector size:

Enter image description here


Every low-level performance related question on Stack Overflow should be required to provide MFLOPS information for the whole range of cache relevant data sizes! It's a waste of everybody's time to think of answers and especially discuss them with others without this information.

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The second loop involves a lot less cache activity, so it's easier for the processor to keep up with the memory demands.

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Imagine you are working on a machine where n was just the right value for it only to be possible to hold two of your arrays in memory at one time, but the total memory available, via disk caching, was still sufficient to hold all four.

Assuming a simple LIFO caching policy, this code:

for(int j=0;j<n;j++){     a[j] += b[j]; } for(int j=0;j<n;j++){     c[j] += d[j]; } 

would first cause a and b to be loaded into RAM and then be worked on entirely in RAM. When the second loop starts, c and d would then be loaded from disk into RAM and operated on.

the other loop

for(int j=0;j<n;j++){     a[j] += b[j];     c[j] += d[j]; } 

will page out two arrays and page in the other two every time around the loop. This would obviously be much slower.

You are probably not seeing disk caching in your tests but you are probably seeing the side effects of some other form of caching.

There seems to be a little confusion/misunderstanding here so I will try to elaborate a little using an example.

Say n = 2 and we are working with bytes. In my scenario we thus have just 4 bytes of RAM and the rest of our memory is significantly slower (say 100 times longer access).

Assuming a fairly dumb caching policy of if the byte is not in the cache, put it there and get the following byte too while we are at it you will get a scenario something like this:

  • With

    for(int j=0;j<n;j++){  a[j] += b[j]; } for(int j=0;j<n;j++){  c[j] += d[j]; } 
  • cache a[0] and a[1] then b[0] and b[1] and set a[0] = a[0] + b[0] in cache - there are now four bytes in cache, a[0], a[1] and b[0], b[1]. Cost = 100 + 100.

  • set a[1] = a[1] + b[1] in cache. Cost = 1 + 1.
  • Repeat for c and d.
  • Total cost = (100 + 100 + 1 + 1) * 2 = 404

  • With

    for(int j=0;j<n;j++){  a[j] += b[j];  c[j] += d[j]; } 
  • cache a[0] and a[1] then b[0] and b[1] and set a[0] = a[0] + b[0] in cache - there are now four bytes in cache, a[0], a[1] and b[0], b[1]. Cost = 100 + 100.

  • eject a[0], a[1], b[0], b[1] from cache and cache c[0] and c[1] then d[0] and d[1] and set c[0] = c[0] + d[0] in cache. Cost = 100 + 100.
  • I suspect you are beginning to see where I am going.
  • Total cost = (100 + 100 + 100 + 100) * 2 = 800

This is a classic cache thrash scenario.

vote vote


It's not because of a different code, but because of caching: RAM is slower than the CPU registers and a cache memory is inside the CPU to avoid to write the RAM every time a variable is changing. But the cache is not big as the RAM is, hence, it maps only a fraction of it.

The first code modifies distant memory addresses alternating them at each loop, thus requiring continuously to invalidate the cache.

The second code don't alternate: it just flow on adjacent addresses twice. This makes all the job to be completed in the cache, invalidating it only after the second loop starts.

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