SHF: Small: Solving the Problems of Scalability and Portability while Maximizing Performance of Multiprecision Scalar and Vector Arithmetic on Clusters of GPUs

SHF：小型：解决可扩展性和可移植性问题，同时最大限度地提高 GPU 集群上多精度标量和矢量算术的性能

• 批准号: 1525754
• 负责人: Charles Weems
• 金额: $ 40万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1525754&HistoricalAwards=false
• 关键词: SHF; Small; Solving; Problems; Scalability

This project extends the PI's prior research into achieving high performance for multiprecision arithmetic utilizing commodity graphics processors (GPUs). Multiprecision (MP) arithmetic has important applications in science, engineering, and mathematics when computations require greater numerical precision than standard computer systems support. It is also an important part of cryptography used in secure internet communication. GPUs can accelerate MP arithmetic by more than two orders of magnitude. However, achieving this performance requires novel algorithms and software tools. The world-record performance for exponentiation achieved under the prior grant will be extended to include floating point vector arithmetic. A new code generation model will enable handling a wider range of precisions across newer generations of graphics processors. Support for clusters of GPUs to work together on larger problems, and practical demonstrations of the effectiveness of MP library such as showing how one GPU can offload decryption work from more than a hundred servers, with higher levels of security than are currently in common use, is being developed. Each generation of GPU architecture requires extensive experimentation and reworking of multiprecision code to obtain a new optimum. Yet the potential benefits of a portable and scalable package could be transformational in certain application areas. This effort extends PI's prior work to include floating point and vectors, and begin the transition to GPU clusters. The result will be a publicly available multi-precision arithmetic package and implementation toolset that enables the scientific community to easily take full advantage of GPU scaling to obtain at least an order of magnitude improvement in performance per dollar and performance per watt over CPUs at the same technology step. The approach relies on a novel set of models for GPU storage that provide a higher level of abstraction over which the code generation tools can search for optimal combinations of algorithm, register/memory layout, and kernel launch geometry for a given precision size and GPU architectural generation to achieve maximum resource utilization.

该项目将PI的先前研究扩展到利用商品图形处理器（GPU）的多次算术算术的高性能。当计算需要比标准的计算机系统支持更高的数值精度时，多重复（MP）算术在科学，工程和数学中具有重要的应用。它也是安全互联网通信中使用的密码学的重要组成部分。 GPU可以将MP算术加速两个以上的数量级加速。但是，实现此性能需要新颖的算法和软件工具。在先前赠款下实现的企业的世界记录表现将扩展到包括浮点矢量算术。一个新的代码生成模型将使在新一代的图形处理器上处理更广泛的精度。支持GPU的群集在更大的问题上共同努力，并实际证明了MP库的有效性，例如显示一个GPU如何从一百多个服务器中卸载解密工作，具有比目前使用的普遍使用更高的安全性。每一代GPU体系结构都需要对多重复代码进行广泛的实验和重新加工，以获得新的最佳选择。然而，在某些应用领域，便携式和可扩展包的潜在好处可能是转化的。这项工作扩展了PI的先前工作，以包括浮点和向量，并开始过渡到GPU群集。结果将是一个公开可用的多精神算术软件包和实施工具集，使科学界能够轻松地充分利用GPU缩放量表，以在同一技术步骤下至少获得每美元的性能和每瓦的绩效的数量级提高。该方法依赖于GPU存储的一组新型模型，该模型提供了更高水平的抽象，代码生成工具可以搜索算法，寄存器/内存布局和内核启动几何形状的最佳组合，以提供给定的精度尺寸和GPU架构生成以实现最大资源利用。