Qilin exploiting parallelism on heterogeneous multiprocessors with adaptive mapping

Heterogeneous multiprocessors are increasingly important in the multi-core era due to their potential for high performance and energy efficiency. In order for software to fully realize this potential, the step that maps computations to processing elements must be as automated as possible. However, t...

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Veröffentlicht in:2009 42nd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) S. 45 - 55
Hauptverfasser: Luk, Chi-Keung, Hong, Sunpyo, Kim, Hyesoon
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 12.12.2009
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
adaptive
Computer architecture
Computer systems organization > Architectures > Parallel architectures > Multiple instruction, multiple data
Computing methodologies > Parallel computing methodologies > Parallel programming languages
Concurrent computing
dynamic compilation
Dynamic programming
Energy consumption
Energy efficiency
GPU
Hardware
heterogeneous
mapping
Multicore
Multicore processing
Parallel processing
Programming profession
Software and its engineering > Software notations and tools > General programming languages > Language types > Parallel programming languages
Software performance
adaptive
mapping
GPU
dynamic compilation
heterogeneous
multicore
ISBN:9781605587981, 1605587982
ISSN:1072-4451
Online-Zugang:Volltext
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Zusammenfassung:Heterogeneous multiprocessors are increasingly important in the multi-core era due to their potential for high performance and energy efficiency. In order for software to fully realize this potential, the step that maps computations to processing elements must be as automated as possible. However, the state-of-the-art approach is to rely on the programmer to specify this mapping manually and statically. This approach is not only labor intensive but also not adaptable to changes in runtime environments like problem sizes and hardware/software configurations. In this study, we propose adaptive mapping, a fully automatic technique to map computations to processing elements on a CPU+GPU machine. We have implemented it in our experimental heterogeneous programming system called Qilin. Our results show that, by judiciously distributing works over the CPU and GPU, automatic adaptive mapping achieves a 25% reduction in execution time and a 20% reduction in energy consumption than static mappings on average for a set of important computation benchmarks. We also demonstrate that our technique is able to adapt to changes in the input problem size and system configuration.
ISBN:9781605587981
1605587982
ISSN:1072-4451
DOI:10.1145/1669112.1669121