HOPPERFISH: Holistic Profiling with Portable Extensible and Robust Framework Intended for Systems with Heterogeneity

Abstract

HOPPERFISH introduces a novel, robust profiling framework explicitly engineered to address the complexities of modern heterogeneous computing systems. The framework achieves holistic system profiling by offering portable and extensible instrumentation across diverse architectural components, including CPUs, GPUs, and specialized accelerators. This tool aims to simplify performance analysis and debugging in complex systems where performance bottlenecks often span multiple architectural boundaries.

Report

Key Highlights

• Holistic System Analysis: HOPPERFISH is designed to provide a unified, system-wide view of performance data, moving beyond component-specific profiling tools.
• Target Environment: Its primary focus is debugging and optimizing systems characterized by heterogeneity—platforms featuring a mix of different core architectures and specialized processing units.
• Framework Attributes: The design prioritizes portability (working across various OSes and hardware configurations), extensibility (allowing easy integration of new metrics or accelerators), and robustness.
• Core Function: Serves as a crucial piece of infrastructure for performance engineering in complex System-on-Chips (SoCs).

Technical Details

• Methodology: The framework implements 'Holistic Profiling,' likely relying on highly synchronized time-stamping mechanisms and cross-component event correlation to accurately attribute latency and bottlenecks across the entire execution pipeline.
• Architecture Support: Specifically targets environments with non-uniform architectures, implying support for integrating performance monitoring unit (PMU) data from proprietary accelerators alongside standard general-purpose cores.
• Implementation Goals: The emphasis on 'Extensible' suggests a modular design where new hardware interfaces or software traces can be added easily, possibly through a plug-in or driver-based architecture.
• Data Aggregation: The framework must address the challenge of merging disparate data streams (e.g., cache misses on the CPU, throughput on an accelerator, memory accesses via a specialized DMA engine) into a single, coherent profile.

Implications

• Enabling Complex RISC-V Designs: The RISC-V ecosystem thrives on architectural specialization and custom accelerators. However, debugging performance in custom heterogeneous RISC-V SoCs is notoriously difficult due to a lack of standardized holistic tooling.
• Accelerating Optimization: By providing clear, synchronized views of cross-component interactions, HOPPERFISH dramatically reduces the time required for developers and architects to identify and resolve performance bottlenecks in RISC-V-based heterogeneous systems.
• Toolchain Maturity: The introduction of robust, portable profiling tools like HOPPERFISH signals the continued maturation of the broader RISC-V software ecosystem, making the platform more appealing for high-performance computing and complex embedded applications.
• Standardization Potential: If widely adopted, this framework could set a de facto standard for how performance data is collected and visualized across diverse RISC-V implementations.