Only Buffer When You Need To: Reducing On-chip GPU Traffic with Reconfigurable Local Atomic Buffers
Preyesh Dalmia, Rohan Mahapatra, Matthew D. Sinclair
Abstract
In recent years, due to their wide availability and ease of programming, GPUs have emerged as the accelerator of choice for a wide variety of applications including graph analytics and machine learning training. These applications use atomics to update shared global variables. However, since GPUs do not efficiently support atomics, this limits scalability. We propose to use hardware-software co-design to address this bottleneck and improve scalability. At the software level, we leverage recently proposed extensions to the GPU memory consistency model to identify atomic updates where the ordering can be relaxed. For example, in these algorithms the updates are commutative. At the hardware level, we propose a buffering mechanism that extends the reconfigurable local SRAM per SM. By buffering partial updates of these atomics locally, our design increases reuse, reduces atomic serialization cost, and minimizes overhead. Thus, our mechanism alleviates the impact of global atomic updates and improves performance by 28%, energy by 19%, and network traffic by 19% on average and outperforms hLRC and PHI.