HVSM: Hardware‐Variability Aware Streaming Processors’ Management Policy in GPUs
Jingweijia Tana and Kaige Yanb
College of Computer Science and Technology Jilin University
ajtan@jlu.edu.cn
byankaige@jlu.edu.cn
ABSTRACT
GPUs are widely used in general‐purpose high performance computing field due to their highly parallel architecture. In recent years, a new era with nanometer scale integrated circuit manufacture process has come, as a consequence, GPUs’ computation capability gets even stronger. However, as process technology scales down, hardware variability, e.g., process variations (PVs) and negative bias temperature instability (NBTI), has a higher impact on the chip quality. The parallelism of GPU desires high consistency of hardware units on chip, otherwise, the worst unit will inevitably become the bottleneck. So the hardware variability becomes a pressing concern to further improve GPUs’ performance and lifetime, not only in integrated circuit fabrication, but more in GPU architecture design. Streaming Processors (SPs) are the key units in GPUs, which perform most of parallel computing operations. Therefore, in this work, we focus on mitigating the impact of hardware variability in GPU SPs. We first model and analyze SPs’ performance variations under hardware variability. Then, we observe that both PV and NBTI have large impact on SP’s performance. We further observe unbalanced SP utilization, e.g., some SPs are idle when others are active, during program execution. Leveraging both observations, we propose a Hardware Variability‐aware SPs’ Management policy (HVSM), which dynamically prioritizes the fast SPs, regroups SPs in a two‐level granularity and dispatches computation in appropriate SPs. Our experimental results show HVSM effectively reduces the impact of hardware variability, which can translate to 28% performance improvement or 14.4% life time extension for a GPU chip.
