XPS:CLCCA: Optimizing Heterogeneous Platforms for Unstructured Parallelism

重大的社会和经济变革正受到“大数据”出现的推动。“在经济的各个领域，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，其中大规模并行越来越非结构化，层次化，工作负载依赖性和时变。 与此同时，能量和功率考虑正在将计算机架构推向大规模并行异构组织，诸如与诸如通用图形处理单元（GPU）的批量同步并行（BSP）架构紧密集成的多线程CPU。这种由能源效率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，可以利用高能效异构体系结构的吞吐量潜力，用于处理大量关系数据集的紧急应用程序。在这些领域中的应用程序所表现出的非结构化动态并行性抑制了实现并行异构体系结构的潜力。本研究开发一套协调的算法，编译器和微架构技术，有效地利用动态并行。该技术套件能够有效地导航并行性、局部性和数据移动之间的权衡，以实现优化的高性能实现。 首先，该计划利用稀疏线性代数的语言来制定算法，以暴露大量的非结构化并行。其次，这种提法驱动新的编译器和运行时系统优化，这些新兴的应用程序和异构硬件的计算特性。第三，在微体系结构层次上，我们提出了新的内存层次管理技术，专门利用动态并行。集成解决方案（算法，编译器/运行时和微架构）在商品平台上展示，并以开源软件堆栈的形式提供，以支持和实现社区范围内的研究工作。对于美国企业来说，要利用异构体系结构和系统的新功能来开发新兴应用程序，就必须创造新技术，并培养具备利用这些技术所需技能的员工。 将通过格鲁吉亚理工学院的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）促进技术转让和劳动力影响，该中心的成员包括英特尔、IBM、惠普和AMD，以及面向应用的公司，如LogicBlox和洲际商品交易所（ICE），还有能源部国家实验室，如桑迪亚和橡树岭。格鲁吉亚理工学院的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.