Intel Math Kernel Library

Wang, Endong; Zhang, Qing; Shen, Bo; Zhang, Guangyong; Lu, Xiaowei; Wu, Qingxiang; Wang, Yajuan

doi:10.1007/978-3-319-06486-4_7

Cited by 250 publications

(119 citation statements)

References 35 publications

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“…In this code it can be observed that in the first loop accesses to vector b components and accesses to vector c in the second To try to solve this problem different mathematical libraries including Preconditioned Conjugate Gradient solvers were tested. So, Intel MKL [4], ViennaCL [9] and cuSparse [8] were introduced and the implementations of PCG using a Jacobian preconditioner were executed on a 16-core AMD Opteron (tm) processor 6376 with a Nvidia GTX Titan GK 110 for a 800 × 800 cell map.…”

Section: Windninja Performance Analysis and Sparse Matrix Storagementioning

confidence: 99%

Applying vectorization of diagonal sparse matrix to accelerate wind field calculation

et al. 2016

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Wind field calculation is a critical issue in reaching accurate forest fire propagation predictions. However, when the involved terrain map is large, the amount of memory and the execution time can prevent them from being useful in an operational environment. Wind field calculation involves sparse matrices that are usually stored in CSR storage format. This storage format can cause sparse matrix-vector multiplications to create a bottleneck due to the number of cache misses involved. Moreover, the matrices involved are extremely sparse and follow a very well-defined pattern. Therefore, a new storage system has been designed to reduce memory requirements and cache misses in this particular sparse matrix-vector multiplication. Sparse matrixvector multiplication has been implemented using this new storage format and taking advantage of the inherent parallelism of the operation. The new method has been implemented in OpenMP, MPI and CUDA and has been tested on different hardware configurations. The results are very promising and the execution time and memory requirements are significantly reduced.

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Section: Windninja Performance Analysis and Sparse Matrix Storagementioning

confidence: 99%

Applying vectorization of diagonal sparse matrix to accelerate wind field calculation

et al. 2016

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“…Math Kernel Library (MKL) is an optimized library for Intel processors, designed to solve engineering, science, and financial problems (Wang et al, 2014a). It contains packages to perform linear algebra (BLAS and LAPACK), fast Fourier transform, statistics, data fitting, and vector calculations.…”

Section: Solver Improvementmentioning

confidence: 99%

“…It contains packages to perform linear algebra (BLAS and LAPACK), fast Fourier transform, statistics, data fitting, and vector calculations. MKL was chosen as the numerical solver because of its speed and its ability to perform operations in parallel (Wang et al, 2014a).…”

Section: Solver Improvementmentioning

confidence: 99%

An interactive tool for rapid biventricular analysis of congenital heart disease

Gilbert

Lam

Pontré

et al. 2015

Clin Physio Funct Imaging

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SummaryCardiac malformations are the most common birth defect. Better interventions in early life have improved mortality for children with congenital heart disease, but heart failure is a significant problem in adulthood. These patients require regular imaging and analysis of biventricular (left and right ventricular) function. In this study, we describe a rapid method to analyse left and right ventricular shape and function from cardiac MRI examinations. A 4D (3D+time) finite element model template is interactively customized to the anatomy and motion of the biventricular unit. The method was validated in 17 patients and 10 ex‐vivo hearts. Interactive model updates were achieved through preconditioned conjugate gradient optimization on a multithread system, and by precomputing points predicted from a coarse mesh optimization.

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“…Typically, BLAS implementations are tuned to take advantage of multicore parallelism and the memory hierarchy to achieve good performance when dealing with large amounts of data [Wang et al 2014]. While BLAS implementations deal with both sparse and dense data representations, non-stride-1 (interleaved) dense data ac- cess is difficult to optimize in a library context.…”

Section: Comparison With Hand-tuned and Reference Blasmentioning

confidence: 99%

Automatic Vectorization of Interleaved Data Revisited

Anderson

Malik

Gregg

2015

ACM Trans. Archit. Code Optim.

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Automatically exploiting short vector instructions sets (SSE, AVX, NEON) is a critically important task for optimizing compilers. Vector instructions typically work best on data that is contiguous in memory, and operating on non-contiguous data requires additional work to gather and scatter the data. There are several varieties of non-contiguous access, including interleaved data access. An existing approach used by GCC generates extremely efficient code for loops with power-of-two interleaving factors (strides). In this paper we propose a generalization of this approach that produces similar code for any compile-time constant interleaving factor. In addition, we propose several novel program transformations which were made possible by our generalized representation of the problem. Experiments show that our approach achieves significant speedups for both power-of-two and non-power-of-two interleaving factors. Our vectorization approach results in mean speedups over scalar code of 1.77x on Intel SSE and 2.53x on Intel AVX2 in real-world benchmarking on a selection of BLAS Level 1 routines. On the same benchmark programs, GCC 5.0 achieves mean improvements of 1.43x on Intel SSE and 1.30x on Intel AVX2. In synthetic benchmarking on Intel SSE, our maximum improvement on data movement is over 4x for gathering operations and over 6x for scattering operations versus scalar code.

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Intel Math Kernel Library

Cited by 250 publications

References 35 publications

Applying vectorization of diagonal sparse matrix to accelerate wind field calculation

Applying vectorization of diagonal sparse matrix to accelerate wind field calculation

An interactive tool for rapid biventricular analysis of congenital heart disease

Automatic Vectorization of Interleaved Data Revisited

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