AMOEBA: A Coarse Grained Reconfigurable Architecture for Dynamic GPU Scaling

TL;DR

AMOEBA is a reconfigurable GPU architecture that dynamically adjusts Streaming Multi-processors (SMs) to optimize performance for diverse applications, achieving up to 4.3x speedup and 47% average improvement.

Contribution

It introduces a microarchitecture-based reconfigurable SM design that predicts application scalability and adapts SM configurations at runtime without extra programming effort.

Findings

Achieves up to 4.3x performance gain on benchmarks.

Provides an average of 47% performance improvement across tests.

Enables dynamic creation of heterogeneous SMs.

Abstract

Different GPU applications exhibit varying scalability patterns with network-on-chip (NoC), coalescing, memory and control divergence, and L1 cache behavior. A GPU consists of several StreamingMulti-processors (SMs) that collectively determine how shared resources are partitioned and accessed. Recent years have seen divergent paths in SM scaling towards scale-up (fewer, larger SMs) vs. scale-out (more, smaller SMs). However, neither scaling up nor scaling out can meet the scalability requirement of all applications running on a given GPU system, which inevitably results in performance degradation and resource under-utilization for some applications. In this work, we investigate major design parameters that influence GPU scaling. We then propose AMOEBA, a solution to GPU scaling through reconfigurable SM cores. AMOEBA monitors and predicts application scalability at run-time and adjusts the SM configuration to meet program requirements. AMOEBA also enables dynamic creation of heterogeneous SMs through independent fusing or splitting. AMOEBA is a microarchitecture-based solution and requires no additional programming effort or custom compiler support. Our experimental evaluations with application programs from various benchmark suites indicate that AMOEBA is able to achieve a maximum performance gain of 4.3x, and generates an average performance improvement of 47% when considering all benchmarks tested.