high performance computing on graphics processing units: hgpu.org

State-of-art graphics processing units (GPUs) employ the single-instruction multiple-data (SIMD) style execution to achieve both high computational throughput and energy efficiency. As previous works have shown, there exists significant computational redundancy in SIMD execution, where different execution lanes operate on the same operand values. Such value locality is referred to as uniform vectors. In this paper, we first show that besides redundancy within a uniform vector, different vectors can also have the identical values. Then, we propose detailed architecture designs to exploit both types of redundancy. For redundancy within a uniform vector, we propose to either extend the vector register file with token bits or add a separate small scalar register file to eliminate redundant computations as well as redundant data storage. For redundancy across different uniform vectors, we adopt instruction reuse, proposed originally for CPU architectures, to detect and eliminate redundancy. The elimination of redundant computations and data storage leads to both significant energy savings and performance improvement. Furthermore, we propose to leverage such redundancy to protect arithmetic-logic units (ALUs) and register files against hardware errors. Our detailed evaluation shows that our proposed design has low hardware overhead and achieves performance gains, up to 23.9% and 12.0% on average, along with energy savings, up to 24.8% and 12.6% on average, as well as a 21.1% and 14.1% protection coverage for ALUs and register files, respectively.