Performance considerations in Java

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Performance considerations
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In almost every application of this pattern, especially when used with OpenMP, the assumption is made that the program will execute on a computer that has multiple PEs sharing a single address space This address space is assumed to provide equal time access to every element of memory Unfortunately, this is usually not the case Memories on modern computers are hierarchical There are caches on the PEs, memory modules packaged with subsets of PEs, and other complications While great effort is made in designing shared memory multiprocessor computers to make them act like symmetric multiprocessor (SMP) computers, the fact is that all shared memory computers display
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some degree of nonuniformity in memory access times across the system In many cases, these effects are of secondary concern, and we can ignore how a program's memory access patterns match up with the target system's memory hierarchy In other cases, particularly on larger shared memory machines, programs must be explicitly organized according to the needs of the memory hierarchy The most common trick is to make sure the data access patterns during initialization of key data structures match those during later computation using these data structures This is discussed in more detail in [Mat03, NA01] and later in this pattern as part of the mesh computation example Another performance problem is false sharing This occurs when variables are not shared between UEs, but happen to reside on the same cache line Hence, even though the program semantics implies independence, each access by each UE requires movement of a cache line between UEs This can create huge overheads as cache lines are repeatedly invalidated and moved between UEs as these supposedly independent variables are updated An example of a program fragment that would incur high levels of false sharing is shown in Fig 523 In this code, we have a pair of nested loops The outermost loop has a small iteration count that will map onto the number of UEs (which we assume is four in this case) The innermost loop runs over a large number of time consuming iterations Assuming the iterations of the innermost loop are roughly equal, this loop should parallelize effectively But the updates to the elements of the A array inside the innermost loop mean each update requires the UE in question to own the indicated cache line Although the elements of A are truly independent between UEs, they likely sit in the same cache line Hence, every iteration in the innermost loop incurs an expensive cache line invalidate and movement operation It is not uncommon for this to not only destroy all parallel speedup, but to even cause the parallel program to become slower as more PEs are added The solution is to create a temporary variable on each thread to accumulate values in the innermost loop False sharing is still a factor, but only for the much smaller outermost loop where the performance impact is negligible
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Figure 523 Program fragment showing an example of false sharing The small array A is held in one or two cache lines As the UEs access A inside the innermost loop, they will need to take ownership of the cache line back from the other UEs This back-andforth movement of the cache lines destroys performance The solution is to use a temporary variable inside the innermost loop
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#include <omph> #define N 4 // Assume this equals the number of UEs #define M 1000 extern double work(int, int); // a time-consuming function int main() { int i, j; double A[N] = {00}; // Initialize the array to zero // method one: a loop with false sharing from A since the elements // of A are likely to reside in the same cache line #pragma omp parallel for private(j,i) for(j=0; j<N; j++){ for(i=0; i<M; i++){
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A[j] += work(i,j);
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// method two: remove the false sharing by using a temporary // private variable in the innermost loop double temp; #pragma omp parallel for private(j,i, temp) for(j=0; j<N; j++){ temp = 00; for(i=0; i<M;i++){ temp += work(i,j); } A[j] += temp; } return 0; }
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Examples As examples of this pattern in action, we will briefly consider the following:
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Numerical integration to estimate the value of a definite integral using the trapezoid rule Molecular dynamics, nonbonded energy computation Mandelbrot set computation Mesh computation
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Each of these examples has been described elsewhere in detail We will restrict our discussion in this pattern to the key loops and how they can be parallelized
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