II-NEW: A Heterogeneous Testbed for Exploring Emerging HPC Tools, Programming Languages, and Applications

并行计算正处于令人兴奋的变化之中。几乎所有新的处理器都有多个内核，所有应用程序都可以并行处理。 GPU是许多计算密集型应用程序的首选处理器，使异构性成为HPC领域的另一个现实。 这些特性对应用程序开发人员和系统级软件架构师提出了坚韧的挑战。 该项目计划开发基础设施，以支持新兴HPC系统的编程模型，工具和运行时系统的研究。该项目创建了一个具有新兴高端系统核心方面的异构计算集群。通过将每个节点的核心最大化到32个，系统将具有分布在适度数量的节点上的高度并行性。它利用多个异构CPU架构并部署计算加速器。存储系统基于固态硬盘（SSD），这是一种新兴技术，允许比磁性硬盘驱动器低两个数量级的延迟。 对该系统进行的研究集中在执行工具链的各个方面。支持的具体研究项目包括：用于多核架构和加速器的OpenMP编译器和运行时系统;异构环境中计算和通信操作的自动优化;异构和志愿者环境中的高度容错执行;以及低延迟存储设备的并行I/O操作的优化。 此外，与应用科学家的合作有望为各种应用领域开发高级并行化策略。

Parallel computing is in the midst of exciting changes. Virtually all new processors have multiple cores exposing all applications to parallelism. GPUs are processors of choice for many compute intensive applications, making heterogeneity another reality of the HPC landscape. These characteristics pose tough challenges to application developers as well as system level software architects. This project plans to develop the infrastructure to support research into programming models, tools and runtime systems for emerging HPC systems.The project creates a heterogeneous compute cluster with core aspects of emerging high-end systems. The system will have a high degree of parallelism distributed on a modest number of nodes by maximizing the cores per node up to 32. It utilizes multiple heterogeneous CPU architectures and deploy compute accelerators. The storage system is based on solid-state disks (SSDs), an emerging technology that allows two orders of magnitude lower latencies than magnetic hard drives. The research conducted on this system focuses on various aspects of the execution tool chain. Specific research projects supported include: OpenMP compiler and run-time system for multi-core architectures and accelerators; automatic optimization of compute and communication operations in heterogeneous environments; highly fault-tolerant execution in heterogeneous and volunteer environments; and optimization of parallel I/O operations for low latency storage devices. Furthermore, collaborative work with applications scientists is expected to lead to the development of advanced parallelization strategies for various application domains.