This work reviews the experience of implementing different versions of the SSPR rank-one update operation of the BLAS library. The main objective was to contrast CPU versus GPU implementation effort and complexity of an optimized BLAS routine, not considering performance. This work contributes with a sample procedure to compare BLAS kernel implementations, how to start using GPU libraries and offloading, how to analyze their performance and the issues faced and how they were solved.WPDP- XIII Workshop procesamiento distribuido y paraleloRed de Universidades con Carreras en Informática (RedUNCI
A current trend in high-performance computing is to decompose a large linear algebra problem into ba...
SuperLU_DIST is a distributed memory parallel solver for sparse linear systems. The solver makes sev...
One of the key areas for enabling users to efficiently use an HPC system is providing optimized BLAS...
We provide timing results for common linear algebra subroutines across BLAS (Basic Lin-ear Algebra S...
International audienceIn the last ten years, GPUs have dominated the market considering the computin...
The increase in performance of the last generations of graphics processors (GPUs) has made this clas...
Scientific applications are some of the most computationally demanding software pieces. Their core i...
This dataset contains the execution time of four BLAS Level 1 operations - ASUM, DOT, SCAL and AXPY ...
Abstract. Implementations of the Basic Linear Algebra Subprograms (BLAS) interface are major buildin...
The Gram-Schmidt method is a classical method for determining QR decompositions, which is commonly u...
This dataset contains the execution time of four BLAS Level 1 operations - ASUM, DOT, SCAL and AXPY ...
As Central Processing Units (CPUs) and Graphical Processing Units (GPUs) get progressively better, d...
Kernel methods such as kernel principal component analysis and support vector machines have become p...
International audienceNowadays GPUs have dominated the market considering the computing/power metric...
Basic Linear Algebra Subprograms (BLAS) and Linear Algebra Package (LAPACK) form basic building bloc...
A current trend in high-performance computing is to decompose a large linear algebra problem into ba...
SuperLU_DIST is a distributed memory parallel solver for sparse linear systems. The solver makes sev...
One of the key areas for enabling users to efficiently use an HPC system is providing optimized BLAS...
We provide timing results for common linear algebra subroutines across BLAS (Basic Lin-ear Algebra S...
International audienceIn the last ten years, GPUs have dominated the market considering the computin...
The increase in performance of the last generations of graphics processors (GPUs) has made this clas...
Scientific applications are some of the most computationally demanding software pieces. Their core i...
This dataset contains the execution time of four BLAS Level 1 operations - ASUM, DOT, SCAL and AXPY ...
Abstract. Implementations of the Basic Linear Algebra Subprograms (BLAS) interface are major buildin...
The Gram-Schmidt method is a classical method for determining QR decompositions, which is commonly u...
This dataset contains the execution time of four BLAS Level 1 operations - ASUM, DOT, SCAL and AXPY ...
As Central Processing Units (CPUs) and Graphical Processing Units (GPUs) get progressively better, d...
Kernel methods such as kernel principal component analysis and support vector machines have become p...
International audienceNowadays GPUs have dominated the market considering the computing/power metric...
Basic Linear Algebra Subprograms (BLAS) and Linear Algebra Package (LAPACK) form basic building bloc...
A current trend in high-performance computing is to decompose a large linear algebra problem into ba...
SuperLU_DIST is a distributed memory parallel solver for sparse linear systems. The solver makes sev...
One of the key areas for enabling users to efficiently use an HPC system is providing optimized BLAS...