high performance computing on graphics processing units: hgpu.org

Two decades of microprocessor architecture driven by quantitative 90/10 optimization has delivered an extraordinary 1000-fold improvement in microprocessor performance, enabled by transistor scaling which improved density, speed, and energy. Recent generations of technology have produced limited benefits in transistor speed and power, so as a result the industry has turned to multicore parallelism for performance scaling . Long-range studies [1, 2] indicate that radical approaches are needed in the coming decade – extreme parallelism, near-threshold voltage scaling (and resulting poor single-thread performance), and tolerance of extreme variability – are required to maximize energy efficiency and compute density. These changes create major new challenges in architecture and software. As a result, the performance and energy-efficiency advantages of heterogeneous architectures are increasingly attractive. However, computing has lacked an optimization paradigm in which to systematically analyze, assess, and implement heterogeneous computing. We propose a new paradigm, "10×10", which clusters applications into a set of less frequent cases (ie. 10% cases), and creates customized architecture, implementation, and software solutions for each of these clusters, achieving significantly better energy efficiency and performance. We call this approach "10×10" because the approach is exemplified by optimizing ten different 10% cases, reflecting a shift away from the 90/10 optimization paradigm framed by Amdahl’s law [3]. We describe the 10×10 approach, explain how it solves the major obstacles to widespread adoption of heterogeneous architectures, and present a preliminary 10×10 clustering, strawman architecture, and software tool chain approach.