XPS:CLCCA: Optimizing Heterogeneous Platforms for Unstructured Parallelism

“大数据”的出现正在推动重大的社会和经济变革。在经济的所有部门中，企业越来越依赖于从大量关系数据集中提取有用情报的能力。紧急应用程序的特点是数据密集型计算，其中大量并行性日益是非结构化的、分层的、工作负载相关的和时变的。与此同时，能源和电力方面的考虑正在推动计算机体系结构向大规模并行异构组织发展，例如多线程cpu与批量同步并行（BSP）体系结构紧密集成，例如通用图形处理单元（gpu）。这种由能效问题驱动的演变对现代软件堆栈产生了破坏性影响，挑战了我们提取处理大数据所需性能的能力。我们需要开发计算技术，以利用能源高效异构架构的吞吐量潜力，用于处理大量关系数据集的紧急应用程序。实现大规模并行异构架构的潜力受到这些领域应用程序所表现出的非结构化动态并行性的抑制。本研究开发了一套有效利用动态并行的协调算法、编译器和微架构技术。这套技术能够有效地在并行性、局部性和数据移动之间进行权衡，从而实现优化的高性能实现。首先，所提出的程序利用稀疏线性代数语言来制定算法，以暴露大量非结构化并行性。其次，这个公式驱动新的编译器和运行时系统优化，以适应这些紧急应用程序和异构硬件的计算特性。第三，在微体系结构层面，我们提出了新的内存层次管理技术，以利用动态并行性。集成的解决方案（算法、编译器/运行时和微架构）在商品平台上进行了演示，并以开源软件堆栈的形式交付，以支持和实现社区范围的研究工作。对于美国企业来说，为新兴的应用程序开发异构架构和系统的新功能，创造新技术和拥有利用这些技术的必要技能的员工是至关重要的。技术转移和劳动力影响将通过佐治亚理工学院的NSF工业大学计算机系统实验研究合作研究中心（CERCS, www.cercs.gatech.edu）来促进，其成员包括英特尔、IBM、惠普和AMD，以及LogicBlox和洲际商品交易所（ICE）等面向应用的公司，以及能源部国家实验室桑迪亚和橡树岭等。佐治亚理工学院的NVIDIA卓越中心预计也会产生类似的影响。

Major social and economic change is being driven by the emergence of "big data." In all sectors of the economy businesses are increasingly relying on the ability to extract useful intelligence from massive relational data sets. Emergent applications are characterized by data intensive computation where massive parallelism is increasingly unstructured, hierarchical, workload dependent, and time varying. At the same time, energy and power considerations are driving computer architecture towards massively parallel heterogeneous organizations such as multithreaded CPUs tightly integrated with bulk synchronous parallel (BSP) architectures such as general-purpose graphics processing units (GPUs). This evolution driven by energy efficiency concerns has had a disruptive impact on modern software stacks challenging our ability to extract the performance necessary to deal with big data. We need to develop computing technologies that can harness the throughput potential of energy efficient heterogeneous architectures for emergent applications processing massive relational data sets. Realizing the potential of massively-parallel heterogeneous architectures is inhibited by the unstructured dynamic parallelism exhibited by applications in these domains. This research develops a suite of coordinated algorithm, compiler, and microarchitecture technologies that effectively exploits dynamic parallelism. The suite of techniques enables the effective navigation of the tradeoffs between parallelism, locality, and data movement to realize optimized high performance implementations. First, the proposed program utilizes the language of sparse linear algebra to formulate algorithms to expose massive unstructured parallelism. Second, this formulation drives new compiler and run-time system optimizations tailored to the computational characteristics of these emergent applications and heterogeneous hardware. Third, at the microarchitecture level we propose new memory hierarchy management techniques tailored to exploiting dynamic parallelism. The integrated solutions (algorithm, compiler/run-time, and microarchitecture) are demonstrated on commodity platforms and delivered in the form of an open source software stack to support and enable community wide research efforts. For U.S. businesses to exploit the new capabilities of heterogeneous architectures and systems for emerging applications, it is essential to both create new technology and employees with the necessary skills to utilize these technologies. Technology transfer and workforce impact will be promoted through the NSF Industry University Cooperative Research Center on Experimental Research in Computer Systems (CERCS, www.cercs.gatech.edu) at Georgia Tech with members such as Intel, IBM, HP, and AMD as well as application oriented companies such as LogicBlox and Intercontinental Commodity Exchange (ICE) and also Department of Energy National laboratories such as Sandia and Oak Ridge. Similar impacts are expected through the NVIDIA Center of Excellence at Georgia Tech.