Accelerating graph applications on integrated GPU platforms via instrumentation-driven optimizations

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.