The Configuration Wall: Characterization and Elimination of Accelerator Configuration Overhead

TL;DR

This work addresses the configuration wall in accelerator-augmented compute by introducing a Configuration Roofline Model that separates configuration from compute and memory bottlenecks. It then proposes a compiler abstraction, accfg (MLIR dialect), to track accelerator state and enable two key optimizations: configuration deduplication and configuration-computation overlap. Implemented in MLIR/xDSL and evaluated on Gemmini and OpenGeMM, the approach yields substantial gains—roughly 11% on Gemmini and about 2x on OpenGeMM—by moving workloads out of the configuration-bound region and toward compute-bound operation. The results demonstrate that compiler-assisted automation can significantly mitigate configuration overhead, enabling more effective use of hardware accelerators in diverse systems.

Abstract

Contemporary compute platforms increasingly offload compute kernels from CPU to integrated hardware accelerators to reach maximum performance per Watt. Unfortunately, the time the CPU spends on setup control and synchronization has increased with growing accelerator complexity. For systems with complex accelerators, this means that performance can be configuration-bound. Faster accelerators are more severely impacted by this overlooked performance drop, which we call the configuration wall. Prior work evidences this wall and proposes ad-hoc solutions to reduce configuration overhead. However, these solutions are not universally applicable, nor do they offer comprehensive insights into the underlying causes of performance degradation. In this work, we first introduce a widely-applicable variant of the well-known roofline model to quantify when system performance is configuration-bound. To move systems out of the performance-bound region, we subsequently propose a domain-specific compiler abstraction and associated optimization passes. We implement the abstraction and passes in the MLIR compiler framework to run optimized binaries on open-source architectures to prove its effectiveness and generality. Experiments demonstrate a geomean performance boost of 2x on the open-source OpenGeMM system, by eliminating redundant configuration cycles and by automatically hiding the remaining configuration cycles. Our work provides key insights in how accelerator performance is affected by setup mechanisms, thereby facilitating automatic code generation for circumventing the configuration wall.

Figures (12)

• Figure 1: An accelerator's maximum attainable performance is not just limited by its memory bandwidth and compute resources, but also by its configuration interface.
• Figure 2: This timeline of a typical program executed on a host CPU with dedicated hardware accelerator shows configuration overhead: cycles where CPU and accelerator are not performing any useful work. We present compiler techniques to eliminate this overhead, by making the configuration time shorter, and by overlapping it with other computations.
• Figure 3: The processor roofline model, proposed in rooflinemodel, can be used to quickly check whether an algorithm with operational intensity ${I}_\text{Operational}$ is compute-bound or memory-bound on a multi-core processor with peak performance $P_{\text{Peak}}$ and peak memory bandwidth ${BW}_{\text{Memory}}$.
• Figure 4: We propose the configuration roofline plot, which shows the maximum performance for systems with certain configuration capabilities. Sequential configuration systems and concurrent configuration systems are bound in performance by the light red and dark red lines respectively. The knee point in the middle divides the plot in two distinct regions, the configuration bound on the left and compute bound region on the right. On the left, timeline visualizations for either configuration capability are given in the configuration bound region (B), knee point (C) and compute bound region (A).
• Figure 5: The processor and configuration roofline can be combined into a "roofsurface", that indicates whether an accelerator's performance is limited by configuration, peak memory bandwidth, or peak performance.