GPUs rely on large register files to unlock thread-level parallelism for high throughput. Unfortunately, large register files are power hungry, making it important to seek for new approaches to improve their utilization. This paper introduces a new register file organization for efficient register-packing of narrow integer and floating-point operands designed to leverage on advances in static analysis. We show that the hardware/software co-designed register file organization yields a performance improvement of up to 79%, and 18.6%, on average, at a modest output-quality degradation.

中文翻译：

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使用静态数据压缩的 GPU 寄存器文件

GPU 依靠大型寄存器文件来解锁线程级并行性以实现高吞吐量。不幸的是，大型寄存器文件非常耗电，因此寻找新方法来提高其利用率非常重要。本文介绍了一种新的寄存器文件组织，用于对窄整数和浮点操作数进行有效的寄存器打包，旨在利用静态分析的进步。我们表明，硬件/软件共同设计的寄存器文件组织在适度的输出质量下降的情况下，平均提高了 79% 和 18.6% 的性能提升。