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2017 GTC San Jose

S7267 - Automatic Compiler-Based Optimization of Graph Analytics for the GPU

Session Speakers
Session Description

Learn how to use IrGL, our newly developed language and compiler, to obtain high-speed graph algorithm implementations without writing a lot of low-level NVIDIA CUDA. IrGL can be used for parallel graph algorithm research, graph analytics, and graph database query processing. IrGL performance for graph algorithms meets or exceeds the performance of low-level handwritten CUDA code because our optimizing compiler automatically tackles three key challenges encountered in writing graph algorithms -- atomics, load imbalance due to serialization of loops, and kernel launch throughput -- freeing up the programmer to focus on higher-level optimizations. We'll introduce the IrGL language, its compiler, and how they can use IrGL to target problems with irregular data-parallelism.


Additional Session Information
Intermediate
Talk
Accelerated Analytics Performance Optimization Programming Languages
Cloud Services Defense Higher Education / Research Retail / Etail
50 minutes
Session Schedule