GbA: A graph‐based thread partition approach in speculative multithreading

Abstract

SummarySpeculative multithreading is an effective technique to automatically parallelize sequential programs. Conventional thread partition approaches primarily include heuristic rule–based (HR‐based) and machine learning–based. Heuristic rule–based approaches are effective to parallelize one type of programs and can seldom obtain the respective optimal partitions for different programs, and existing machine learning–based approaches usually use vector‐based characterization to represent a program, but easily ignore control information among basic blocks and partitions along other paths except the critical path. This paper proposes a novel graph‐based thread partition approach to overcome these 2 bottlenecks. Our approach characterizes programs with graphs, integrating feature and control informations, extracting good partition scheme successfully, and also applies a machine‐learning algorithm to predict partition for unseen programs. Prophet, which consists of an automatic parallelization compiler and a multicore simulator, evaluates the performance of multithreaded programs. Experiment results reveal that our approach delivers a maximum performance improvement of about 55.49% on an 8 core than HR‐based approach and a maximum 97.67% performance improvement over HR‐based partition for SPEC2000 benchmarks. This result suggests that graph‐based thread partition approach is effective for thread partition in speculative multithreading.

A Graph Partition-Based Large-Scale Distribution Network Reconfiguration Method

“…And they proposed a novel graph-based thread partitioning method to overcome these two bottlenecks. They used graphs to characterize the program, integrated features and control information, and successfully extracted a good partition scheme [ 4 ]. Ghasemi said that the problem of power distribution system reconfiguration is a complex optimization process to find a structure with the least loss, which needs to meet the satisfaction of both consumers and the power distribution system company.…”

IDaTPA:Importance Degree Based Thread Partitioning Approach in Thread Level Speculation

“…In recent years, the researches [1,[36][37][38][39][40][41][42][43] introduced various machine learning approaches to the parallelism of programs. Wang et al [40] made use of a machine learning method to map a parallelized program to multi-core processors with an automatic compiler-based approach.…”

“…Before determining the best workload allocation scheme, they classified programs with static program features on the foundation of its prior experience with similar programs. The paper [36] shew a novel graph-based thread partition approach, firstly to use graph to characterize programs, including data and control information, as well as static and dynamic features; secondly to learn partition knowledge as well as to predict partition for unseen programs. In short, the characteristics of previous studies can be classified into two headings: firstly, the procedures in one program use the same partition scheme; second, the final partition unit is one program, rather than a procedure.…”