Add to ORKGThe use of auto-tuning techniques in a matrix multiplication routine for hybrid CPU+GPU platforms is analyzed. Basic models of the execution time of the hybrid routine and information obtained during its installation are used to optimize the execution time with a balanced assignation of the computation to the computing components in the heterogeneous system. Satisfactory results are obtained, with experimental execution times close to the lowest achievabl
As users and developers, we are witnessing the opening of a new computing scenario: the introduction...
The trend in computer architectures has for several years been heterogeneous systems consisting of a...
We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and mul...
The use of auto-tuning techniques in a matrix multiplication routine for hybrid CPU+GPU platforms i...
AbstractThe introduction of auto-tuning techniques in linear algebra routines using hybrid combinati...
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...
Graphics hardware’s performance is advancing much faster than the performance of conventional microp...
Proceedings of: Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016...
International audienceCurrent compilers cannot generate code that can compete with hand-tuned code i...
AbstractThis paper presents results of our study on double-precision general matrix-matrix multiplic...
Ovaj rad opisuje program kojim se uspoređuje množenje matrica na različitim arhitekturama. U detalj ...
The development of high performance dense linear algebra (DLA) critically depends on highly optimize...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
As users and developers, we are witnessing the opening of a new computing scenario: the introduction...
The trend in computer architectures has for several years been heterogeneous systems consisting of a...
We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and mul...
The use of auto-tuning techniques in a matrix multiplication routine for hybrid CPU+GPU platforms i...
AbstractThe introduction of auto-tuning techniques in linear algebra routines using hybrid combinati...
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...
Graphics hardware’s performance is advancing much faster than the performance of conventional microp...
Proceedings of: Third International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2016...
International audienceCurrent compilers cannot generate code that can compete with hand-tuned code i...
AbstractThis paper presents results of our study on double-precision general matrix-matrix multiplic...
Ovaj rad opisuje program kojim se uspoređuje množenje matrica na različitim arhitekturama. U detalj ...
The development of high performance dense linear algebra (DLA) critically depends on highly optimize...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
For the past decade, power/energy consumption has become a limiting factor for large-scale and embed...
As users and developers, we are witnessing the opening of a new computing scenario: the introduction...
The trend in computer architectures has for several years been heterogeneous systems consisting of a...
We present a new approach to utilizing all CPU cores and all GPUs on heterogeneous multicore and mul...