COLAB: Collaborative and Efficient Processing of Replicated Cache Requests in GPU
Part Of
Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC
Start Page
314
End Page
319
ISBN (of the container)
978-145039783-4
Date Issued
2023-01-16
Author(s)
Abstract
In this work, we aim to capture replicated cache requests between Stream Multiprocessors (SMs) within an SM cluster to alleviate the Network-on-Chip (NoC) congestion problem of modern GPUs. To achieve this objective, we incorporate a per-cluster Cache line Ownership Lookup tABle (COLAB) that keeps track of which SM within a cluster holds a copy of a specific cache line. With the assistance of COLAB, SMs can collaboratively and efficiently process replicated cache requests within SM clusters by redirecting them according to the ownership information stored in COLAB. By servicing replicated cache requests within SM clusters that would otherwise consume precious NoC bandwidth, the heavy pressure on the NoC interconnection can be eased. Our experimental results demonstrate that the adoption of COLAB can indeed alleviate the excessive NoC pressure caused by replicated cache requests, and improve the overall system throughput of the baseline GPU while incurring minimal overhead. On average, COLAB can reduce 38% of the NoC traffic and improve instructions per cycle (IPC) by 43%.
Event(s)
28th Asia and South Pacific Design Automation Conference, ASP-DAC 2023
Publisher
ACM
Type
conference paper