Optimization Techniques for GPU-Based Parallel Programming Models in High-Performance

This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units,it delves into the nuanced optimization of memory access,thread management,algorithmic design,and data structures.These optimizations are critical for exploiting the parallel processing capabilities of GPUs,addressingboth the theoretical frameworks and practical implementations.By integrating advanced strategies such as memory coalescing,dynamic scheduling,and parallel algorithmic transformations,this research aims to significantly elevate computational efficiency and throughput.The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains,highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments.The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers,fostering advancements in computational sciences and technology.

Programming bare-metal accelerators with heterogeneous threading models:a case study of Matrix-3000

As the hardware industry moves toward using specialized heterogeneous many-core processors to avoid the effects of the power wall,software developers are finding it hard to deal with the complexity of these systems.In this paper,we share our experience of developing a programming model and its supporting compiler and libraries for Matrix-3000,which is designed for next-generation exascale supercomputers but has a complex memory hierarchy and processor organization.To assist its software development,we have developed a software stack from scratch that includes a low-level programming interface and a high-level OpenCL compiler.Our low-level programming model offers native programming support for using the bare-metal accelerators of Matrix-3000,while the high-level model allows programmers to use the OpenCL programming standard.We detail our design choices and highlight the lessons learned from developing system software to enable the programming of bare-metal accelerators.Our programming models have been deployed in the production environment of an exascale prototype system.

Optimization Strategies Oriented to Loop Characteristics in Software Thread Level Speculation Systems

Thread level speculation provides not only a simple parallel programming model, but also an effective mech- anism for thread-level parallelism exploitation. The performance of software speculative parallel models is limited by high global overheads caused by different types of loops. These loops usually have different characteristics of dependencies and different requirements of optimization strategies. In this paper, we propose three comprehensive optimization techniques to reduce different factors of global overheads, aiming at requirements from different types of loops. Inter-thread fetching can reduce the high mis-speculation rate of the loops with frequent dependencies and out-of-order committing can reduce the control overhead of the loops with infrequent dependencies, while enhanced dynamic task granularity resizing can reduce the control overhead and optimize the global overhead of the loops with changing characteristics of dependencies. All these three optimization techniques have been implemented in HEUSPEC~ a software TLS system. Experimental results indicate that they can satisfy tile demands from different groups of benchmarks. The combination of these techniques can improve the performance of all benchmarks and reach a higher average speedup.