Vector Extensions in COTS Processors to Increase Guaranteed Performance in Real-Time Systems

Abstract

The need for increased application performance in high-integrity systems such as those in avionics is on the rise as software continues to implement more complex functionalities. The prevalent computing solution for future high-integrity embedded products is multi-processor systems-on-chip (MPSoC) processors. MPSoCs include central processing unit (CPU) multicores that enable improving performance via thread-level parallelism. MPSoCs also include generic accelerators (graphics processing units [GPUs]) and application-specific accelerators. However, the data processing approach (DPA) required to exploit each of these underlying parallel hardware blocks carries several open challenges to enable the safe deployment in high-integrity domains. The main challenges include the qualification of its associated runtime system and the difficulties in analyzing programs deploying the DPA with out-of-the-box timing analysis and code coverage tools. In this work, we perform a thorough analysis of vector extensions (VExts) in current commercial off-the-shelf (COTS) processors for high-integrity systems. We show that VExts prevent many of the challenges arising with parallel programming models and GPUs. Unlike other DPAs, VExts require no runtime support, prevent design race conditions that might arise with parallel programming models, and have minimum impact on the software ecosystem, enabling the use of existing code coverage and timing analysis tools. We develop vectorized versions of neural network kernels and show that the NVIDIA Xavier VExts provide a reasonable increase in guaranteed application performance of up to 2.7x. Our analysis contends that VExts are the DPA approach with arguably the fastest path for adoption in high-integrity systems.