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 explores optimizing offload performance in heterogeneous MPSoCs through a co-design of hardware and software, reducing computation offloading overheads.
It introduces an approach to minimize synchronization and communication overheads, using the Manticore MPSoC platform for implementation.
Results show a significant reduction in offload overheads and application performance improvement, with a developed runtime model achieving a MAPE lower than 1%.
The study highlights the potential for hardware-software co-design in enhancing data-parallel processing in heterogeneous MPSoCs and outlines directions for future research.
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 study 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 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.
The researchers implemented their study 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.
The experimental results showcased a significant reduction in offload overheads and an improvement in application performance:
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.
The study 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.