%0 Journal Article
%J International Journal of High Performance Computing Applications
%D 2015
%T Batched matrix computations on hardware accelerators based on GPUs
%A Azzam Haidar
%A Tingxing Dong
%A Piotr Luszczek
%A Stanimire Tomov
%A Jack Dongarra
%K batched factorization
%K hardware accelerators
%K numerical linear algebra
%K numerical software libraries
%K one-sided factorization algorithms
%X Scientific applications require solvers that work on many small size problems that are independent from each other. At the same time, the high-end hardware evolves rapidly and becomes ever more throughput-oriented and thus there is an increasing need for an effective approach to develop energy-efficient, high-performance codes for these small matrix problems that we call batched factorizations. The many applications that need this functionality could especially benefit from the use of GPUs, which currently are four to five times more energy efficient than multicore CPUs on important scientific workloads. This paper, consequently, describes the development of the most common, one-sided factorizations, Cholesky, LU, and QR, for a set of small dense matrices. The algorithms we present together with their implementations are, by design, inherently parallel. In particular, our approach is based on representing the process as a sequence of batched BLAS routines that are executed entirely on a GPU. Importantly, this is unlike the LAPACK and the hybrid MAGMA factorization algorithms that work under drastically different assumptions of hardware design and efficiency of execution of the various computational kernels involved in the implementation. Thus, our approach is more efficient than what works for a combination of multicore CPUs and GPUs for the problems sizes of interest of the application use cases. The paradigm where upon a single chip (a GPU or a CPU) factorizes a single problem at a time is not at all efficient in our applications’ context. We illustrate all of these claims through a detailed performance analysis. With the help of profiling and tracing tools, we guide our development of batched factorizations to achieve up to two-fold speedup and three-fold better energy efficiency as compared against our highly optimized batched CPU implementations based on MKL library. The tested system featured two sockets of Intel Sandy Bridge CPUs and we compared with a batched LU factorizations featured in the CUBLAS library for GPUs, we achieve as high as 2.5× speedup on the NVIDIA K40 GPU.
%B International Journal of High Performance Computing Applications
%8 2015-02
%G eng
%R 10.1177/1094342014567546
%0 Conference Paper
%B 8th Workshop on General Purpose Processing Using GPUs (GPGPU 8)
%D 2015
%T Optimization for Performance and Energy for Batched Matrix Computations on GPUs
%A Azzam Haidar
%A Tingxing Dong
%A Piotr Luszczek
%A Stanimire Tomov
%A Jack Dongarra
%K batched factorization
%K hardware accelerators
%K numerical linear algebra
%K numerical software libraries
%K one-sided factorization algorithms
%X As modern hardware keeps evolving, an increasingly effective approach to develop energy efficient and high-performance solvers is to design them to work on many small size independent problems. Many applications already need this functionality, especially for GPUs, which are known to be currently about four to five times more energy efficient than multicore CPUs. We describe the development of the main one-sided factorizations that work for a set of small dense matrices in parallel, and we illustrate our techniques on the LU and Cholesky factorizations. We refer to this mode of operation as a batched factorization. Our approach is based on representing the algorithms as a sequence of batched BLAS routines for GPU-only execution. The goal of avoiding multicore CPU use, e.g., as in the hybrid CPU-GPU algorithms, is to exclusively benefit from the GPU’s significantly higher energy efficiency, as well as from the removal of the costly CPU-to-GPU communications. Furthermore, we do not use a single symmetric multiprocessor (on the GPU) to factorize a single problem at a time. We illustrate how our performance analysis and the use of profiling and tracing tools guided the development and optimization of batched factorizations to achieve up to 2-fold speedup and 3-fold better energy efficiency compared to our highly optimized batched CPU implementations based on the MKL library (when using two sockets of Intel Sandy Bridge CPUs). Compared to a batched LU factorization featured in the CUBLAS library for GPUs, we achieved up to 2.5 speedup on the K40 GPU.
%B 8th Workshop on General Purpose Processing Using GPUs (GPGPU 8)
%I ACM
%C San Francisco, CA
%8 2015-02
%G eng
%R 10.1145/2716282.2716288
%0 Conference Paper
%B 8th Workshop on General Purpose Processing Using GPUs (GPGPU 8) co-located with PPOPP 2015
%D 2015
%T Towards Batched Linear Solvers on Accelerated Hardware Platforms
%A Azzam Haidar
%A Piotr Luszczek
%A Stanimire Tomov
%A Jack Dongarra
%K batched factorization
%K hardware accelerators
%K numerical linear algebra
%K numerical software libraries
%K one-sided factorization algorithms
%X As hardware evolves, an increasingly effective approach to develop energy efficient, high-performance solvers, is to design them to work on many small and independent problems. Indeed, many applications already need this functionality, especially for GPUs, which are known to be currently about four to five times more energy efficient than multicore CPUs for every floating-point operation. In this paper, we describe the development of the main one-sided factorizations: LU, QR, and Cholesky; that are needed for a set of small dense matrices to work in parallel. We refer to such algorithms as batched factorizations. Our approach is based on representing the algorithms as a sequence of batched BLAS routines for GPU-contained execution. Note that this is similar in functionality to the LAPACK and the hybrid MAGMA algorithms for large-matrix factorizations. But it is different from a straightforward approach, whereby each of GPU’s symmetric multiprocessors factorizes a single problem at a time.We illustrate how our performance analysis together with the profiling and tracing tools guided the development of batched factorizations to achieve up to 2-fold speedup and 3-fold better energy efficiency compared to our highly optimized batched CPU implementations based on the MKL library on a two-sockets, Intel Sandy Bridge server. Compared to a batched LU factorization featured in the NVIDIA’s CUBLAS library for GPUs, we achieves up to 2.5-fold speedup on the K40 GPU.
%B 8th Workshop on General Purpose Processing Using GPUs (GPGPU 8) co-located with PPOPP 2015
%I ACM
%C San Francisco, CA
%8 2015-02
%G eng