SARIS: Accelerating Stencil Computations on Energy-Efficient RISC-V Compute Clusters with Indirect Stream Registers

Core Concepts

SARIS is a generic and flexible methodology that leverages indirect stream registers to accelerate stencil computations, achieving significant speedups, near-ideal FPU utilizations, and energy efficiency improvements on an energy-efficient RISC-V compute cluster.

Abstract

The paper presents SARIS, a generic and highly flexible methodology for accelerating stencil computations using register-mapped indirect streams. SARIS encodes the offsets of grid elements accessed in the loop body of stencil codes in index arrays and reuses these indices on each point update, using the point's coordinates as an indirection base. This approach minimizes memory access overheads and enables near-ideal floating-point unit (FPU) utilizations. The authors implement optimized baseline and SARIS-accelerated parallel stencil codes on the open-source, energy-efficient RISC-V Snitch compute cluster, which features eight RV32G cores extended with sparse stream semantic registers (SSSRs) and a hardware loop (FREP) extension. In cycle-accurate simulations, the SARIS-accelerated codes achieve significant speedups of 2.72x, near-ideal FPU utilizations of 81%, and energy efficiency improvements of 1.58x on average over the baseline codes. The authors also estimate the performance benefits of SARIS on a 256-core manycore system with a bandwidth-limiting HBM2E memory stack. They find an average FPU utilization of 64%, an average speedup of 2.14x, and up to 15% higher fractions of peak compute than a leading GPU code generator, despite the memory system's bandwidth and latency limitations.

Stats

SARIS achieves a geomean speedup of 2.72x over baseline codes. SARIS achieves near-ideal FPU utilizations of 81% on average. SARIS achieves energy efficiency improvements of 1.58x on average over baseline codes. On a 256-core manycore system, SARIS achieves an average FPU utilization of 64% and an average speedup of 2.14x. SARIS achieves up to 15% higher fractions of peak compute than a leading GPU code generator on the 256-core manycore system.

Quotes

"SARIS is a generic and highly flexible methodology for stencil acceleration using register-mapped indirect streams." "SARIS encodes the offsets of grid elements accessed in the loop body of stencil codes in index arrays and reuses these indices on each point update, using the point's coordinates as an indirection base." "SARIS-accelerated codes achieve significant speedups of 2.72x, near-ideal FPU utilizations of 81%, and energy efficiency improvements of 1.58x on average over baseline codes."

Key Insights Distilled From

SARIS

by Paul Scheffl... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05303.pdf

Deeper Inquiries

How could the SARIS methodology be extended to accelerate other types of data-parallel computations beyond stencil codes

The SARIS methodology can be extended to accelerate other types of data-parallel computations beyond stencil codes by adapting the concept of register-mapped indirect streams to suit the specific characteristics of different algorithms. For instance, for convolutional neural networks (CNNs), SARIS could be applied to optimize the data access patterns for convolutional layers. By mapping the input feature maps and filter weights to indirect streams and efficiently managing the memory accesses, SARIS could enhance the performance of CNN computations. Similarly, for graph algorithms like PageRank or graph traversal, SARIS could be utilized to streamline memory accesses and improve data locality by mapping graph nodes and edges to indirect streams. The key lies in identifying the repetitive data access patterns in the algorithm and leveraging the flexibility of SARIS to optimize these patterns for efficient computation.

What are the potential limitations or challenges in automatically applying the SARIS approach through compiler techniques, and how could these be addressed

Automatically applying the SARIS approach through compiler techniques may face challenges related to the complexity of identifying the optimal index arrays and stream mappings for different algorithms. One limitation could be the variability in data access patterns across different computations, making it challenging for a compiler to generalize the SARIS optimization. Additionally, the overhead of index array initialization and management could impact the overall performance gains if not optimized effectively. To address these challenges, compiler techniques could incorporate advanced static analysis and profiling to identify recurring data access patterns and generate optimized index arrays automatically. Moreover, dynamic runtime adaptation could be implemented to adjust the index arrays based on the specific workload characteristics, ensuring efficient utilization of indirect streams. By integrating machine learning algorithms into the compiler optimization process, the compiler could learn from past optimizations and adapt the SARIS methodology more effectively to a wide range of data-parallel computations.

Given the performance benefits of SARIS, how might it impact the design and architecture of future energy-efficient RISC-V compute clusters or manycore systems

The performance benefits of SARIS could significantly impact the design and architecture of future energy-efficient RISC-V compute clusters or manycore systems by influencing key aspects such as memory hierarchy, data access patterns, and compute efficiency. Future systems could be designed with built-in support for indirect stream registers (SRs) and affine SRs to enable efficient data streaming and maximize bandwidth utilization. The memory subsystem could be optimized to handle the increased demand for streaming data efficiently, potentially incorporating specialized memory units for streaming data access. Additionally, the core architecture could be enhanced to better support the concurrent use of multiple indirect SRs, enabling higher FPU utilization and improved performance for data-parallel computations. Overall, SARIS could drive the evolution of energy-efficient compute clusters towards more streamlined and optimized architectures tailored for data-intensive workloads.