Abstract

This paper investigates two approaches—hardware acceleration and pure software implementation—for incorporating modern, non-SPMD warp-level features into RISC-V GPUs, specifically using the Vortex architecture. The evaluation demonstrates that implementing these features in hardware provides a significant performance gain, achieving up to a 4 times geometric mean IPC speedup in tested microbenchmarks. However, the study confirms that software-based solutions offer a viable and necessary alternative for designs that are sensitive to area and hardware complexity constraints.

Report

Structured Report: Hardware vs. Software Implementation of Warp-Level Features in Vortex RISC-V GPU

Key Highlights

• The research addresses the necessity of supporting modern warp-level features in RISC-V GPUs, which increasingly diverge from the traditional SPMD (Single Program Multiple Data) model.
• The study compares dedicated hardware implementation against software-only solutions for handling these complex GPU features within the Vortex RISC-V GPU architecture.
• Hardware implementation provided a substantial performance benefit, achieving an up to 4 times geomean IPC speedup in microbenchmark testing.
• Software-based approaches are validated as a practical choice for scenarios where design complexity or area constraints are paramount.

Technical Details

• Target Architecture: Vortex RISC-V GPU.
• Core Challenge: Implementing warp-level features that necessitate non-SPMD behavior, which is typically difficult to achieve efficiently on conventional GPU paradigms.
• Performance Metric: Geomean IPC (Instructions Per Cycle) speedup was used to quantify the performance difference between the implementations.
• Result Magnitude: Hardware acceleration showed a performance gain of up to 4x geomean IPC compared to the equivalent software implementation.

Implications

• RISC-V GPU Maturity: This work advances the functional maturity of RISC-V GPUs by demonstrating effective methods for integrating essential, non-traditional GPU features crucial for modern programming models.
• Design Trade-offs: The research provides critical quantitative data for hardware architects, clearly defining the performance premium (4x IPC) that can be gained by sacrificing area for dedicated hardware support.
• Ecosystem Flexibility: By validating both hardware and software paths, the paper ensures flexibility for the RISC-V ecosystem, allowing different implementations based on target constraints (e.g., maximum performance for accelerators vs. minimal area/power for embedded systems).