Abstract
In recent years, GPGPUs have experienced tremendous growth as general-purpose and high-throughput computing devices. However, irregular applications cannot fully utilize the hardware resource because of their plenty of control-flow divergences, irregular memory accesses and load imbalances. The lack of in-depth characterization and quantifying the ways in which irregular applications differ from regular ones on GPGPUs has prevented users from effectively making use of the hardware resource. We examine a suite of representative irregular applications on a cycle-accurate GPU simulator. We characterize their performance aspects and analyze the bottlenecks. We also assess the impact of changes in cache, DRAM and interconnect and discuss the implications for GPU architecture design. This work is useful in understanding and optimizing irregular applications on GPUs.
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