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The performance of graph programs depends highly on the algorithm, the sizeand structure of the input graphs, as well as the features of the underlyinghardware. No single set of optimizations or one hardware platform works wellacross all settings. To achieve high performance, the programmer must carefullyselect which set of optimizations and hardware platforms to use. The GraphItprogramming language makes it easy for the programmer to write the algorithmonce and optimize it for different inputs using a scheduling language. However,GraphIt currently has no support for generating high performance code for GPUs.Programmers must resort to re-implementing the entire algorithm from scratch ina low-level language with an entirely different set of abstractions andoptimizations in order to achieve high performance on GPUs.  We propose GG, an extension to the GraphIt compiler framework, that achieveshigh performance on both CPUs and GPUs using the same algorithm specification.GG significantly expands the optimization space of GPU graph processingframeworks with a novel GPU scheduling language and compiler that enablescombining graph optimizations for GPUs. GG also introduces two performanceoptimizations, Edge-based Thread Warps CTAs load balancing (ETWC) andEdgeBlocking, to expand the optimization space for GPUs. ETWC improves loadbalancing by dynamically partitioning the edges of each vertex into blocks thatare assigned to threads, warps, and CTAs for execution. EdgeBlocking improvesthe locality of the program by reordering the edges and restricting randommemory accesses to fit within the L2 cache. We evaluate GG on 5 algorithms and9 input graphs on both Pascal and Volta generation NVIDIA GPUs, and show thatit achieves up to 5.11x speedup over state-of-the-art GPU graph processingframeworks, and is the fastest on 66 out of the 90 experiments.

Compilation Techniques for Graph Algorithms on GPUs