Name
Title | ||
|---|---|---|
Performance and Power Characteristics of Matrix Multiplication Algorithms on Multicore and Shared Memory Machines |
Abstract | ||
|---|---|---|
For many scientific applications, dense matrix multiplication is one of the most important and computation intensive linear algebra operations. An efficient matrix multiplication on high performance and parallel computers requires optimizations on how matrices are decomposed and exchanged between computational nodes to reduce communication and synchronization overhead, as well as to efficiently exploit the memory hierarchy within a node to improve both spatial and temporal data locality. In this paper, we presented our studies of performance, cache behavior, and energy efficiency of multiple parallel matrix multiplication algorithms on a multicore desktop computer and a medium-size shared memory machine, both being considered as referenced sizes of nodes to create a medium- and largescale computational clusters for high performance computing used in industry and national laboratories. Our results highlight both the performance and energy efficiencies, and also provide implications on the memory and resources pressures of those algorithms. We hope this could help users choose the appropriate implementations according to their specific data sets when composing larger-scale scientific applications that use parallel matrix multiplication kernels on a node. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/SC.Companion.2012.87 | SC Companion |
Keywords | Field | DocType |
high performance,large-scale computational clusters,matrix multiplication,matrix multiplication algorithms,computational nodes,power aware computing,linear algebra operations,parallel computers,medium-scale computational clusters,multiple parallel matrix multiplication algorithms,cache storage,parallel matrix multiplication kernel,matrix multiplication algorithm power characteristics,software performance evaluation,cache behavior,memory hierarchy,mathematics computing,high performance computing,communication overhead reduction,parallel algorithms,shared memory systems,temporal data locality improvement,spatial data locality improvement,parallel matrix multiplication kernels,shared memory machines,efficient matrix multiplication,multicore desktop computer,multiple parallel matrix multiplication,parallel computer,medium-size shared memory machine,multicore system,high-performance computers,dense matrix multiplication,synchronization overhead reduction,energy efficiency,matrix multiplication algorithm performance characteristics,scientific applications,synchronisation,power characteristics | Cache-oblivious algorithm,Memory hierarchy,Shared memory,Computer science,Cache,Parallel algorithm,Parallel computing,Algorithm,Multiplication,Matrix multiplication,Sparse matrix,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-4673-6218-4 | 1 | 0.35 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yonghong Yan | 1 | 656 | 114.13 |
| Jeremy Kemp | 2 | 1 | 0.69 |
| Xiaonan Tian | 3 | 41 | 5.30 |
| Abid Muslim Malik | 4 | 12 | 4.12 |
| Barbara Chapman | 5 | 163 | 14.63 |