Optimizing Offload Performance in Heterogeneous MPSoCs (2404.01908v1)

Abstract: Heterogeneous multi-core architectures combine a few "host" cores, optimized for single-thread performance, with many small energy-efficient "accelerator" cores for data-parallel processing, on a single chip. Offloading a computation to the many-core acceleration fabric introduces a communication and synchronization cost which reduces the speedup attainable on the accelerator, particularly for small and fine-grained parallel tasks. We demonstrate that by co-designing the hardware and offload routines, we can increase the speedup of an offloaded DAXPY kernel by as much as 47.9%. Furthermore, we show that it is possible to accurately model the runtime of an offloaded application, accounting for the offload overheads, with as low as 1% MAPE error, enabling optimal offload decisions under offload execution time constraints.

• The paper introduces a hardware-software co-design method that significantly reduces offload overheads in heterogeneous MPSoCs.
• It demonstrates a 47.9% speedup in DAXPY kernel execution and presents an accurate runtime model with a sub-1% error rate.
• The approach enables optimal offload decisions, paving the way for enhanced performance in fine-grained heterogeneous computing.

Optimizing Offload Performance in Heterogeneous MPSoCs through Hardware and Software Co-Design

Introduction to Heterogeneous Computing and Offloading

In the evolving landscape of computing architecture, heterogeneous multi-processor systems-on-chip (MPSoCs) present a significant advancement in improving system performance. These systems leverage a combination of high-performance host cores with energy-efficient accelerator cores. The paper conducted by Luca Colagrande and Luca Benini focuses on optimizing the offload performance in such heterogeneous MPSoCs. Their work introduces a novel approach to reduce overheads associated with computation offloading through the co-design of hardware and offload routines, enabling significant performance improvements and facilitating optimal offload decisions.

Offloading Challenges and Previous Work

Offloading computations from a host to an accelerator core incurs overheads due to the need for synchronization and communication, diminishing the potential speedup, especially in small and fine-grained parallel tasks. While previous studies have explored the quantification of offload overheads, they have not provided a comprehensive solution or an accurate model for estimating these overheads. Moreover, existing research has primarily focused on discrete CPU-GPU architectures, lacking in-depth analysis applicable to integrated MPSoC environments.

Methodology and Implementation on Manticore MPSoC

The researchers implemented their paper on the open-source Manticore MPSoC platform, which combines a CVA6 host core with a multi-cluster accelerator fabric. By extending Manticore's interconnect and memory subsystem to support multicast communication and integrating a dedicated synchronization unit, they aimed to reduce the overheads involved in offloading tasks to the accelerator.

Key Findings and Results

The experimental results showcased a significant reduction in offload overheads and an improvement in application performance:

• Overhead Reduction and Performance Improvement: The co-design approach enabled a decrease in offload overheads, with a 47.9% speedup observed in the execution of a DAXPY kernel, by optimizing the communication and synchronization processes.
• Accurate Runtime Model: An accurate model was developed to estimate the runtime of offloaded tasks, considering the reduced overheads. This model achieved a Mean Absolute Percentage Error (MAPE) lower than 1%, demonstrating its effectiveness in predicting offload performance.

Implications and Future Directions

This research illustrates the potential of hardware-software co-design in enhancing the effectiveness of heterogeneous MPSoCs. By mitigating offload overheads, it is possible to leverage the full capabilities of accelerator cores for data-parallel processing, making fine-grained heterogeneous execution more feasible. The proposed runtime model further aids in making informed offloading decisions, optimizing performance under specific execution time constraints.

Given the promising results, future work could explore the extension of this co-design approach to other types of tasks and heterogeneous architectures. Additionally, further refinement of the runtime model could accommodate a wider range of applications and offloading scenarios, broadening the impact of this research on the development of next-generation heterogeneous computing systems.

Conclusion

The paper by Colagrande and Benini represents a significant step forward in addressing the challenges associated with offloading in heterogeneous MPSoC environments. Through a novel co-design approach, they have demonstrated the possibility of substantially reducing offload overheads and improving application performance, supported by the development of an accurate runtime model for optimal offload decisions. This work lays a strong foundation for future advancements in heterogeneous computing, with implications for both practical system optimization and theoretical understanding of offloading dynamics in MPSoCs.