Publications

This is part 2 of a 3-part educational series of publications introducing select topics on optimization of applications for Intel’s multi-core and manycore architectures (Intel Xeon processors and Intel Xeon Phi coprocessors). In this paper we discuss data parallelism. Our focus is automatic vectorization and exposing vectorization opportunities to the compiler. For a practical illustration, we construct and optimize a micro-kernel for particle binning particles. Similar workloads occur applications in Monte Carlo simulations, particle physics software, and statistical analysis. The optimization technique discussed in this paper leads to code vectorization, which results in an order of magnitude performance improvement on an Intel Xeon processor. Performance on Xeon Phi compared to that on a high-end Xeon is 1.4x greater in single precision and 1.6x greater in double precision. See also: Part 1: Multi-Threading and Parallel Reduction Part 2: Strip-Mining for Vectorization Part 3: False Sharing and Padding Complete paper:  Colfax_Optimization_Techniques_2_of_3.pdf (650 KB) [...]

This is part 1 of a 3-part educational series of publications introducing select topics on optimization of applications for the Intel multi-core and manycore architectures (Intel Xeon processors and Intel Xeon Phi coprocessors). In this paper we focus on thread parallelism and race conditions. We discuss the usage of mutexes in OpenMP to resolve race conditions. We also show how to implement efficient parallel reduction using thread-private storage and mutexes. For a practical illustration, we construct and optimize a micro-kernel for binning particles based on their coordinates. Workloads like this one occur in such applications as Monte Carlo simulations, particle physics software, and statistical analysis. The optimization technique discussed in this paper leads to a performance increase of 25x on a 24-core CPU and up to 100x on the MIC architecture compared to a single-threaded implementation on the same architectures. In the next publication of this series, we will demonstrate further optimization of this workload, focusing on vectorization. See also: Part 1: Multi-Threading [...]

Common techniques for fine-tuning the performance of automatically vectorized loops in applications for Intel Xeon Phi coprocessors are discussed. These techniques include strength reduction, regularizing the vectorization pattern, data alignment and aligned data hint, and pointer disambiguation. In addition, the loop tiling technique of memory traffic tuning is shown. The optimization methods are illustrated on an example of single-threaded LU decomposition of a single precision matrix of size 128×128. Benchmarks show that the discussed optimizations improve the performance on the coprocessor by a factor of 2.8 compared to the unoptimized code, and by a factor of 1.7 on the multi-core host system, achieving roughly the same performance on the host and on the coprocessor. The code discussed in the paper can be freely downloaded from this page. Complete paper:  Colfax_LU.pdf (604 KB) Source code for Linux: colfax-lu.tgz (17 [...]

In this paper we demonstrate the methodology for parallelizing the computation of large one-dimensional discrete fast Fourier transforms (DFFTs) on multi-core Intel Xeon processors. DFFTs based on the recursive Cooley-Tukey method have to control cache utilization, memory bandwidth and vector hardware usage, and at the same time scale across multiple threads or compute nodes. Our method builds on single-threaded Intel Math Kernel Library (MKL) implementation of DFFT, and uses the Intel Cilk Plus framework for thread parallelism. We demonstrate the ability of Intel Cilk Plus to handle parallel recursion with nested loop-centric parallelism without tuning the code to the number of cores or cache metrics. The result of our work is a library called EFFT that performs 1D DFTs of size 2^N for N>=21 faster than the corresponding Intel MKL parallel DFT implementation by up to 1.5x, and faster than FFTW by up to 2.5x. The code of EFFT is available for free download under the GPLv3 license. This work provides a new efficient DFFT implementation, and at the same time demonstrates an [...]

The paper demonstrates the optimization of the execution environment of a hybrid OpenMP+MPI computational fluid dynamics code (shallow water equation solver) on a cluster enabled with Intel Xeon Phi coprocessors. The discussion includes: Controlling the number and affinity of OpenMP threads to optimize access to memory bandwidth; Tuning the inter-operation of OpenMP and MPI to partition the problem for better data locality; Ordering the MPI ranks in a way that directs some of the traffic into faster communication channels; Using efficient peer-to-peer communication between Xeon Phi coprocessors based on the InfiniBand fabric. With tuning, the application has 90% percent efficiency of parallel scaling up to 8 Intel Xeon Phi coprocessors in 2 compute nodes. For larger problems, scalability is even better, because of the greater computation to communication ratio. However, problems of that size do not fit in the memory of one coprocessor. The performance of the solver on one Intel Xeon Phi coprocessor 7120P exceeds the performance on a dual-socket Intel Xeon E5-2697 v2 CPU by a [...]