CAREER: Programming the Existing and Emerging Memory Systems for Extreme-scale Parallel Performance

高性能计算（HPC）侧重于使用数值模型来模拟复杂的科学和工程现象，如星系、天气和气候、分子相互作用、电网和飞行中的飞机。在接下来的十年里，我们的目标是建立一个高性能计算并行系统，它具有极高的性能（每秒1千兆次浮点运算（1018次））和处理exabyte（1018）数据的能力。然而，实现超大规模性能的最大挑战之一是所谓的硬件内存墙，即CPU执行的计算速度与从内存系统向CPU提供数据的速度之间的差距越来越大（大约慢了x100倍）。现代HPC系统的低性能效率（平均为60%，低至5%）体现了内存墙的影响，因为等待输入数据的到来浪费了大量的计算周期。为了实现硬件的计算潜力，提高现有和未来计算系统的效率和可用性，创建有效的软件解决方案变得非常关键。这样的解决方案将极大地有利于使用并行计算机解决科学和工程问题的广泛学科，并加速科学发现和问题解决，以提高社会生活质量。CAREER项目开发创新的软件技术，以解决现有和新兴存储系统的编程和性能挑战：1)可移植的抽象机器模型，用于编程、编译和执行并行应用程序；2)新的编程接口和模型，用于数据映射、移动和一致性；3)机器感知编译和数据感知调度技术，实现异步任务流执行模型，以隐藏数据移动的延迟。它通过开发以内存为中心的编程范例来解决内存墙的挑战，以帮助实现并行应用程序的极端规模性能，同时对可编程性的损害最小。在教育方面，该项目涉及更广泛的社区，从高性能计算和计算机科学领域的高中开始。

High performance computing (HPC) focuses on using numerical model to simulate complex science and engineering phenomena, such as galaxies, weather and climate, molecular interactions, electric power grids, and aircraft in flight. Over the next decade the goal is to build HPC parallel system capable of extreme-scale performance (one exaflop (1018)operations per second) and processing exabyte (1018) of data. However, one of the biggest challenges of achieving extreme-scale performance is what is known as the hardware memory wall, which is about the growing gap between the speed of computation performed by CPU and the speed of supplying data to the CPU from memory systems (about x100 time slower). The low performance efficiency of modern HPC system (average 60% and could be as low as 5%) manifests the memory wall impact since a huge amount of computation cycles are wasted for waiting for the arrival of input data. It becomes very critical to create effective software solutions for achieving the computation potential of hardware and for improving the efficiency and usability of the existing and future computing system. Such solutions will significantly benefit a broad range of disciplines that use parallel computers to solve scientific and engineering problems, and accelerate scientific discovery and problem solving to improve quality of life of the society. This CAREER project develops innovative software techniques to address the programming and performance challenges of the existing and emerging memory systems: 1) a portable abstract machine model for programming, compiling and executing parallel applications, 2) new programming interface and model for data mapping, movement, and consistency, and 3) machine-aware compilation and data-aware scheduling techniques to realize an asynchronous task flow execution model to hide the latency of data movement. It addresses the memory wall challenge by developing a memory-centric programming paradigm for helping achieve extreme-scale performance of parallel applications with minimum impairment to programmability. For education, the project involves a broader community starting from high school in the area of HPC and computer science.