HEUSPEC: A Software Speculation Parallel Model

Conventional software speculative parallel models are facing challenge due to the increasing number of the processor core and the diversification of the application. The performance of the guest program under the software speculative parallel execution model is closely related to the speculation accuracy, the control overhead and the rollback overhead of the model. In order to improve the speculative accuracy and the load balance, as well as improve the overhead of the conventional model, in this paper, we proposed a novel speculative parallel model named HEUSPEC. The HEUSPEC includes 2 key techniques, the heuristic value prediction(HVP) and the dynamic task granularity resizing(DTGR). We have implemented the runtime system of the model in ANSI C language. The experiment results show that when the speedup of the HEUSPEC model can reach 4.51 on the average (12% higher than conventional model) when speculative depth equals to 7. Besides, it shows good scalability and lower memory cost.