Name
Abstract | ||
|---|---|---|
Heterogeneity is an ever-growing challenge in computing. The clearest example is the increasing popularity of GPUs, and purpose-designed coprocessors such as Intel Xeon Phi. Even disregarding coprocessors, heterogeneity continues to increase with the rise in CPU core counts, adaptive per-core frequencies, and increasingly hierarchical and complex memory systems. Take a system with four memory nodes, associated with four cores each, and four GPUs, each with a distinct address space and tens to hundreds of cores programmed like a bulk-synchronous parallel cluster. In this case, we are effectively programming clusters of miniature constellations in every node. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2628071.2671428 | PACT |
Keywords | Field | DocType |
gpu,heterogeneous,openmp,parallel programming | Address space,Cluster (physics),Locality,Xeon Phi,Computer science,Adaptive system,Parallel computing,Coprocessor,Memory systems,Multi-core processor | Conference |
ISSN | ISBN | Citations |
1089-795X | 978-1-5090-6607-0 | 0 |
PageRank | References | Authors |
0.34 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Scogland | 1 | 85 | 8.24 |
| Wu-chun Feng | 2 | 2812 | 232.50 |