Title
Resource Aggregation for Task-Based Cholesky Factorization on Top of Heterogeneous Machines.
Abstract
Hybrid computing platforms are now commonplace, featuring a large number of CPU cores and accelerators. This trend makes balancing computations between these heterogeneous resources performance critical. In this paper we propose aggregating several CPU cores in order to execute larger parallel tasks and thus improve the load balance between CPUs and accelerators. Additionally, we present our approach to exploit internal parallelism within tasks. This is done by combining two runtime systems: one runtime system to handle the task graph and another one to manage the internal parallelism. We demonstrate the relevance of our approach in the context of the dense Cholesky factorization kernel implemented on top of the StarPU task-based runtime system. We present experimental results showing that our solution outperforms state of the art implementations.
Year
DOI
Venue
2016
10.1007/978-3-319-58943-5_5
Lecture Notes in Computer Science
Keywords
Field
DocType
Multicore,Accelerator,GPU,Heterogeneous computing,Task DAG,Runtime system,Dense linear algebra,Cholesky
Kernel (linear algebra),Load balancing (computing),Computer science,Parallel computing,Symmetric multiprocessor system,Exploit,Multi-core processor,Runtime system,Computation,Distributed computing,Cholesky decomposition
Conference
Volume
ISSN
Citations 
10104
0302-9743
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Terry Cojean194.27
Abdou Guermouche226927.32
A. Hugo300.34
Raymond Namyst4140583.04
Pierre-andré Wacrenier576636.69