Leveraging Parallel Bit Stream and Dynamic Compilation Technology for Big Data Applications

利用并行比特流和动态编译技术实现大数据应用

• 批准号: RGPIN-2015-06361
• 负责人: Cameron, Robert
• 金额: $ 1.31万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=676891
• 关键词: Leveraging; Parallel; Bit; Stream; Dynamic

The proposed research continues the investigation of parallel bit stream technology as a vehicle for dramatically accelerating text processing applications, while incorporating additional technologies to broaden the scope and scale of applications that can be tackled. In particular, extensions to the research along two dimensions are anticipated. The first is to incorporate dynamic***compilation technologies building on the highly regarded LLVM compiler infrastructure. One important research goal of this extension is to investigate the extent to which parallel bit stream and dynamic compilation technology can be applied to develop accelerated text matching and searching algorithms for problems expressed using dynamic instances of text patterns. A second research goal in using dynamic compilation technology is to investigate the development of generic distributable software binaries that can dynamically adapt to take advantage of particular processor architectural features available at runtime. For example, can a generic x86-64 binary be employed in cloud computing deployments to take advantage of Intel AVX2 instructions when available on a particular execution processor, falling back to default SSE2 code in other cases?******The second dimension of the proposed research is to investigate how big data applications involving text processing can be accelerated by combining the bitwise data parallelism of parallel bit stream technology with other forms of chip-, cluster- and grid-level parallelism. In bringing parallel processors to bear in solving big data problems, the holy grail is to be able to achieve an N-fold acceleration of processing using N processors. In general, getting close to this goal is difficult, except perhaps for problems that are known as "embarrassingly parallel" (fundamentally parallel in nature). Limits on scalability of solutions often mean that there are diminishing returns with the addition of processors. However, parallel bit stream and dynamic compilation technologies offer the possibility of considerably increasing the work achieved per processor in N-processor systems. Furthermore, the bitwise data parallelism of parallel bit stream technology presents opportunities for both pipeline and coarse-grained data parallelism on chip multicore processors, GPUs and clusters. The proposed research then is to investigate the techniques for leveraging parallel bit stream technology and assessing the corresponding benefits that may be achieved.**

拟议的研究继续调查并行位流技术作为一种车辆，大大加快文本处理应用程序，同时结合其他技术，以扩大范围和规模的应用程序，可以解决。特别是，扩展到研究沿沿着两个维度的预期。第一个是在高度重视的LLVM编译器基础设施上集成动态 *** 编译技术。该扩展的一个重要研究目标是研究并行位流和动态编译技术在多大程度上可以应用于开发加速文本匹配和搜索算法，以解决使用文本模式的动态实例表示的问题。使用动态编译技术的第二个研究目标是研究通用可分发软件二进制文件的开发，这些软件二进制文件可以动态地适应以利用在运行时可用的特定处理器体系结构特征。 例如，在云计算部署中是否可以使用通用x86-64二进制文件，以便在特定执行处理器上可用时利用英特尔AVX 2指令，在其他情况下回退到默认的SSE 2代码？*****拟议研究的第二个维度是研究如何通过将并行位流技术的按位数据并行性与其他形式的芯片级、集群级和网格级并行性相结合来加速涉及文本处理的大数据应用。在将并行处理器用于解决大数据问题时，圣杯是能够使用N个处理器实现N倍的处理加速。一般来说，接近这个目标是困难的，也许除了那些被称为“基本平行”（本质上是平行的）的问题。解决方案的可伸缩性限制通常意味着随着处理器的增加，收益会逐渐减少。然而，并行位流和动态编译技术提供了可能性，大大增加了在N处理器系统中每个处理器实现的工作。此外，并行位流技术的按位数据并行性为芯片多核处理器、GPU和集群上的流水线和粗粒度数据并行性提供了机会。然后，拟议的研究是调查利用并行比特流技术的技术，并评估可能实现的相应好处。**