Kepler is the newest GPU architecture from NVIDIA, and the GTX 680 is the first commercially available graphics card based on that architecture. Matrix multi-plication is a canonical computational kernel, and often the main target of initial optimization efforts for a new chip. This article presents preliminary results of au-tomatically tuning matrix multiplication kernels for the Kepler architecture using the GTX 680 card.
International audienceIn this paper, we present an approach to estimate GPU applications' performanc...
Graphics Processing Units (GPUs) have revolutionized the computing landscape in the past decade and ...
Recent years have witnessed phenomenal growth in the application, and capabilities of Graphical Proc...
AbstractThis paper presents results of our study on double-precision general matrix-matrix multiplic...
The development of high performance dense linear algebra (DLA) critically depends on highly optimize...
General purpose computing on graphics processing units (GPGPU) is fast becoming a common feature of ...
Modern graphics processing units (GPUs) have been at the leading edge of in-creasing chip-level para...
Graphics hardware's performance is advancing much faster than the performance of conventional microp...
In order to utilize the tremendous computing power of grpahics hardware and to automatically adapt t...
Graphics hardware’s performance is advancing much faster than the performance of conventional microp...
Computing on graphics processors is maybe one of the most important developments in computational sc...
The dissemination of multi-core architectures and the later irruption of massively parallel devices,...
Abstract. Implementations of the Basic Linear Algebra Subprograms (BLAS) interface are major buildin...
In this paper we discuss about our experiences in improving the performance of GEMM (both single and...
General Matrix Multiplication or GEMM kernels take centre place in high performance computing and ma...
International audienceIn this paper, we present an approach to estimate GPU applications' performanc...
Graphics Processing Units (GPUs) have revolutionized the computing landscape in the past decade and ...
Recent years have witnessed phenomenal growth in the application, and capabilities of Graphical Proc...
AbstractThis paper presents results of our study on double-precision general matrix-matrix multiplic...
The development of high performance dense linear algebra (DLA) critically depends on highly optimize...
General purpose computing on graphics processing units (GPGPU) is fast becoming a common feature of ...
Modern graphics processing units (GPUs) have been at the leading edge of in-creasing chip-level para...
Graphics hardware's performance is advancing much faster than the performance of conventional microp...
In order to utilize the tremendous computing power of grpahics hardware and to automatically adapt t...
Graphics hardware’s performance is advancing much faster than the performance of conventional microp...
Computing on graphics processors is maybe one of the most important developments in computational sc...
The dissemination of multi-core architectures and the later irruption of massively parallel devices,...
Abstract. Implementations of the Basic Linear Algebra Subprograms (BLAS) interface are major buildin...
In this paper we discuss about our experiences in improving the performance of GEMM (both single and...
General Matrix Multiplication or GEMM kernels take centre place in high performance computing and ma...
International audienceIn this paper, we present an approach to estimate GPU applications' performanc...
Graphics Processing Units (GPUs) have revolutionized the computing landscape in the past decade and ...
Recent years have witnessed phenomenal growth in the application, and capabilities of Graphical Proc...