Cache and Bandwidth Aware Matrix Multiplication on the GPU
نویسندگان
چکیده
Recent advances in the speed and programmability of consumer level graphics hardware has sparked a flurry of research that goes beyond the realm of image synthesis and computer graphics. We examine the use of the GPU (graphics processing unit) as a tool for scientific computing, by analyzing techniques for performing large matrix multiplies in GPU hardware. An earlier method for multiplying matrices on the GPU suffered from problems of memory bandwidth. This paper examines more efficient algorithms that make the implementation of large matrix multiplication on upcoming GPU architectures more competitive, using only 25% of the memory bandwidth and instructions of previous GPU algorithms.
منابع مشابه
Sparse Matrix-Matrix Multiplication on Multilevel Memory Architectures : Algorithms and Experiments
Architectures with multiple classes of memory media are becoming a common part of mainstream supercomputer deployments. So called multi-level memories offer differing characteristics for each memory component including variation in bandwidth, latency and capacity. This paper investigates the performance of sparse matrix multiplication kernels on two leading highperformance computing architectur...
متن کاملCache-efficient numerical algorithms using graphics hardware
We present cache-efficient algorithms for scientific computations using graphics processing units (GPUs). Our approach is based on mapping the nested loops in the numerical algorithms to the texture mapping hardware and efficiently utilizing GPU caches. This mapping exploits the inherent parallelism, pipelining and high memory bandwidth on GPUs. We further improve the performance of numerical a...
متن کاملOptimal Configuration of GPU Cache Memory to Maximize the Performance
GPU devices offer great performance when dealing with algorithms that require intense computational resources. A developer can configure the L1 cache memory of the latest GPU Kepler architecture with different cache size and cache set associativity, per Streaming Multiprocessors (SM). The performance of the computation intensive algorithms can be affected by these cache parameters. In this pape...
متن کاملA Study of the Potential of Locality-Aware Thread Scheduling for GPUs
Programming models such as CUDA and OpenCL allow the programmer to specify the independence of threads, effectively removing ordering constraints. Still, parallel architectures such as the graphics processing unit (GPU) do not exploit the potential of data-locality enabled by this independence. Therefore, programmers are required to manually perform data-locality optimisations such as memory co...
متن کاملA New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems
Recently, graphics processors (GPUs) have been increasingly leveraged in a variety of scientific computing applications. However, architectural differences between CPUs and GPUs necessitate the development of algorithms that take advantage of GPU hardware. As sparse matrix vector multiplication (SPMV) operations are commonly used in finite element analysis, a new SPMV algorithm and several vari...
متن کامل