Abstract—We report Linpack benchmark results on the TSUBAME supercomputer, a large scale heterogeneous system equipped with NVIDIA Tesla GPUs and ClearSpeed SIMD accelerators. With all of 10,480 Opteron cores, 640 Xeon cores, 648 ClearSpeed accelerators and 624 NVIDIA Tesla GPUs, we have achieved 87.01TFlops, which is the third record as a heterogeneous system in the world. This paper describes careful tuning and load balancing method required to achieve this performance. On the other hand, since the peak speed is 163 TFlops, the efficiency is 53%, which is lower than other systems. This paper also analyses this gap from the aspect of system architecture. I
To provide a better basis for statistics on high-performance computers, we list the sites that have ...
High‐performance Linpack (HPL) is among the most popular benchmarks for evaluating the capabilities ...
The high performance computing landscape is shifting from collections of homogeneous nodes towards h...
Heterogeneous supercomputers with combined general purpose and accel-erated CPUs promise to be the f...
This paper describes the use of CUDA to accelerate the Linpack benchmark on heterogeneous clusters, ...
Abstract—Dense linear algebra has been traditionally used to evaluate the performance and efficiency...
The aim of this project was to encapsulate the needs of computational science applications. Performa...
In this paper we present the design and implementation of the Linpack benchmark for the IBM BladeCen...
High performance computing platform is moving from homogeneous individual unites to heterogeneous sy...
In this paper, we propose an approach to obtaining en-hanced performance of the Linpack benchmark on...
Performance assessment, through High-Performance Linpack (HPL) benchmark, of the quad-core cluster w...
The Linpack benchmark, in particular the High-Performance Linpack (HPL) implementation, has emerged ...
For decades, performance has driven the high-end computing (HEC) community. However, as highlighted ...
In recent years the designs of High Performance Computing (HPC) clusters have become more complex. T...
In recent years the designs of High Performance Computing (HPC) clusters have become more complex. T...
To provide a better basis for statistics on high-performance computers, we list the sites that have ...
High‐performance Linpack (HPL) is among the most popular benchmarks for evaluating the capabilities ...
The high performance computing landscape is shifting from collections of homogeneous nodes towards h...
Heterogeneous supercomputers with combined general purpose and accel-erated CPUs promise to be the f...
This paper describes the use of CUDA to accelerate the Linpack benchmark on heterogeneous clusters, ...
Abstract—Dense linear algebra has been traditionally used to evaluate the performance and efficiency...
The aim of this project was to encapsulate the needs of computational science applications. Performa...
In this paper we present the design and implementation of the Linpack benchmark for the IBM BladeCen...
High performance computing platform is moving from homogeneous individual unites to heterogeneous sy...
In this paper, we propose an approach to obtaining en-hanced performance of the Linpack benchmark on...
Performance assessment, through High-Performance Linpack (HPL) benchmark, of the quad-core cluster w...
The Linpack benchmark, in particular the High-Performance Linpack (HPL) implementation, has emerged ...
For decades, performance has driven the high-end computing (HEC) community. However, as highlighted ...
In recent years the designs of High Performance Computing (HPC) clusters have become more complex. T...
In recent years the designs of High Performance Computing (HPC) clusters have become more complex. T...
To provide a better basis for statistics on high-performance computers, we list the sites that have ...
High‐performance Linpack (HPL) is among the most popular benchmarks for evaluating the capabilities ...
The high performance computing landscape is shifting from collections of homogeneous nodes towards h...