Schedulability Analysis of Non-preemptive Sporadic Gang Tasks on Hardware Accelerators

Abstract

Non-preemptive rigid gang scheduling combines the performance benefits of parallel execution with the low overhead of non-preemptive scheduling and rigid task programming model. This approach appears particularly well-suited for parallel hardware accelerators where the context switch and migration overheads are critical and should be avoided. One of the most notable examples today is Google's Edge Tensor Processing Unit (TPU) used for neural network inference on embedded boards. The paper studies sporadic non-preemptive rigid gang scheduling applied to multi-TPU edge AI accelerators. Each gang task spawns a fixed number of threads that must execute simultaneously on distinct processing units. We consider non-preemptive fixed-priority gang (NP-FP-Gang) scheduling and propose the first carry-in limitation for gang task response time analysis. The gang task carry-in limitation differs from conventional sequential tasks due to the intra-task parallelism. We formulate it as a generalized knapsack problem and develop a linear programming relaxation and a dynamic programming approach to solve the problem under different time complexities. The performance of the proposed schedulability analysis is evaluated through randomly generated synthetic task sets and a case study using neural network benchmarks executed on commercial off-the-shelf multi-TPU edge AI accelerators. The evaluation results show that the proposed response time analysis effectively improves the state of-the-art NP-FP-Gang schedulability test even by 85.7% for the Edge TPU benchmarks in particular.