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Different GPU applications exhibit varying scalability patterns withnetwork-on-chip (NoC), coalescing, memory and control divergence, and L1 cachebehavior. A GPU consists of several StreamingMulti-processors (SMs) thatcollectively determine how shared resources are partitioned and accessed.Recent years have seen divergent paths in SM scaling towards scale-up (fewer,larger SMs) vs. scale-out (more, smaller SMs). However, neither scaling up norscaling out can meet the scalability requirement of all applications running ona given GPU system, which inevitably results in performance degradation andresource under-utilization for some applications. In this work, we investigatemajor design parameters that influence GPU scaling. We then propose AMOEBA, asolution to GPU scaling through reconfigurable SM cores. AMOEBA monitors andpredicts application scalability at run-time and adjusts the SM configurationto meet program requirements. AMOEBA also enables dynamic creation ofheterogeneous SMs through independent fusing or splitting. AMOEBA is amicroarchitecture-based solution and requires no additional programming effortor custom compiler support. Our experimental evaluations with applicationprograms from various benchmark suites indicate that AMOEBA is able to achievea maximum performance gain of 4.3x, and generates an average performanceimprovement of 47% when considering all benchmarks tested.

AMOEBA: A Coarse Grained Reconfigurable Architecture for Dynamic GPU Scaling