Accelerating graph applications on integrated GPU platforms via instrumentation-driven optimizations
| Autor: | Vanish Talwar, Naila Farooqui, Yuan Chen, Indrajit Roy, Karsten Schwan |
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| Rok vydání: | 2016 |
| Předmět: |
010302 applied physics
Resource scheduling business.industry Computer science Computation Workload 02 engineering and technology Parallel computing 01 natural sciences 020202 computer hardware & architecture Scheduling (computing) Embedded system 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Leverage (statistics) Graph (abstract data type) Dynamic instrumentation business Efficient energy use |
| Zdroj: | Conf. Computing Frontiers |
| DOI: | 10.1145/2903150.2903152 |
| Popis: | Integrated GPU platforms are a cost-effective and energy-efficient option for accelerating data-intensive applications. While these platforms have reduced overhead of offloading computation to the GPU and potential for fine-grained resource scheduling, there remain several open challenges. First, substantial application knowledge is required to leverage GPU acceleration capabilities. Second, static application profiling is inadequate for extracting performance from graph applications that exhibit input-dependent, irregular runtime behaviors. Third, naive scheduling of applications on both CPU and GPU devices may degrade performance due to memory contention. We describe Luminar, a runtime, profile-guided approach to accelerating applications on integrated GPU platforms. By using efficient dynamic instrumentation, Luminar informs resource scheduling about current workload properties. Luminar engenders up to 40% improvements for irregular, graph-based applications, plus 21-80% improvements in throughput and from 3-60% improvements in energy efficiency when scheduling a mix of applications. |
| Databáze: | OpenAIRE |
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