Optimizing HPC Applications with Intel® Cluster Tools

Abstract

Here and elsewhere, certain product names may be the property of their respective third parties.xxii ■ IntroduCtIon Use Interprocedural OptimizationAdd the compiler flag -ipo to switch on interprocedural optimization. This will give the compiler a holistic view of the program and open more optimization opportunities for the program as a whole. Note that this will also increase the overall compilation time.Runtime profiling can also increase the chances for the compiler to generate better code. Profile-guided optimization requires a three-stage process. First, compile the application with the compiler flag -prof-gen to instrument the application with profiling code. Second, run the instrumented application with a typical dataset to produce a meaningful profile. Third, feed the compiler with the profile (-prof-use) and let it optimize the code.

Vectorized algorithm for multidimensional Monte Carlo integration on modern GPU, CPU and MIC architectures

“…The first one is strictly sequential and performs x j ← ax j−1 +c, while the second one performs v j ← x j /m and can be vectorized using new SIMD extensions like AVX and AVX-512 which are available in modern multicore and manycore processors [9,27]. It can be enforced by placing the pragma simd before each loop [27]. To optimize memory access the array, v should be allocated using the _mm_malloc() intrinsic.…”

“…To optimize memory access the array, v should be allocated using the _mm_malloc() intrinsic. It works just like the malloc function and additionally allows data alignment [27]. This loop has limited length (i.…”

Language-based vectorization and parallelization using intrinsics, OpenMP, TBB and Cilk Plus

“…Recently, multicore and manycore computer architectures have become very attractive for achieving high-performance execution of scientific applications at relatively low costs [5,13,17]. Modern CPUs and accelerators achieve performance that was recently reached by supercomputers.…”

“…Intel C/C++ compilers and development tools offer many language-based extensions that can be used to simplify the process of developing high-performance parallel programs [6,17]. OpenMP [3,18] is the most popular, but one can consider using Threading Building Blocks (TBB for short) [6,12] or Cilk Plus [5,13].…”

“…Usually it is not sufficient to parallelize applications. For such computer architectures, efficient vectorization is crucial for achieving satisfactory performance [5,17]. Unfortunately, very often compiler-based automatic vectorization is not possible because of some nonobvious data dependencies inside loops [1].…”