Auto-tuned parallel algorithms for hybrid multi-core/many-core processor clusters

适用于混合多核/众核处理器集群的自动调整并行算法

• 批准号: 9173-2011
• 负责人: Dehne, Frank
• 金额: $ 3.57万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634831
• 关键词: Auto; tuned; parallel; algorithms; hybrid

The main goal of parallel computing research is to create enabling technology for solving data intensive and/or computationally hard problems in the Natural Sciences, Engineering, Medical Sciences, and Social Sciences. My research objective is to contribute towards that goal by developing general parallel algorithm design methodologies and parallel algorithms for specific problem areas.For the next funding period, I propose to study auto-tuned parallel algorithms for hybrid multi-core/many-core processor clusters. Such hybrid processor clusters consist of nodes that contain both, multi-core and many-core (GPU) processors. The focus of our proposed fundamental research will be on non-numerical problems such as computational geometry and graph algorithms. These problems often involve irregular data movements and complex data structures that are particularly challenging for parallel computing. I propose to extend this parallel algorithms research for hybrid clusters by integrating the study of auto-tuned parallel methods. The goal of auto-tuning is to design efficient parallel algorithms and software that adapt automatically to different hardware configurations. Rather than developing new parallel algorithms for every new parallel architecture, auto-tuning aims at designing portable parallel algorithms and software for a large class of current and (hopefully) future parallel machines. The auto-tuning approach is currently most advanced for linear algebra and other matrix based problems but there is very little work published so far on auto-tuning for non-numerical problems such as computational geometry and graph algorithms proposed for this project. Due to the irregular data movements and more advanced data structures, auto-tuning parallel algorithms for these problems will be considerably more challenging. To demonstrate the significance of our research and facilitate knowledge transfer, this project also has a research component on parallel scientific computing, focusing on the design and implementation of an auto-tuned parallel protein interaction prediction algorithm for hybrid processor clusters.

并行计算研究的主要目标是创建使能技术，用于解决自然科学、工程学、医学和社会科学中的数据密集型和/或计算困难的问题。我的研究目标是通过开发通用的并行算法设计方法和针对特定问题的并行算法来实现这一目标。在下一个资助期内，我建议研究混合多核/众核处理器集群的自动调整并行算法。这种混合处理器集群由包含多核和众核（GPU）处理器的节点组成。我们建议的基础研究的重点将是非数值问题，如计算几何和图形算法。这些问题通常涉及不规则的数据移动和复杂的数据结构，这对并行计算特别具有挑战性。我建议扩展这种并行算法的混合集群的研究，通过整合自动调整的并行方法的研究。自动调整的目标是设计高效的并行算法和软件，自动适应不同的硬件配置。而不是开发新的并行算法的每一个新的并行架构，自动调优的目的是设计便携式并行算法和软件的一大类当前和（希望）未来的并行机。自动调整的方法是目前最先进的线性代数和其他矩阵为基础的问题，但有很少的工作发表到目前为止，自动调整的非数值问题，如计算几何和图形算法提出了这个项目。由于不规则的数据移动和更先进的数据结构，这些问题的自动调整并行算法将更具挑战性。为了展示我们研究的重要性和促进知识转移，该项目还包括并行科学计算的研究部分，重点是设计和实现一个用于混合处理器集群的自动调整并行蛋白质相互作用预测算法。