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Data Persistence in a Nutshell

Applications often use files to store data from one run to the next, but high-capacity, non-volatile memory devices make it possible to store data more effectively than using a disk-based file system. This article describes how to design your application to take advantage of these memory devices, thereby avoiding the need for files to serve as persistent memory.
作者: 最后更新时间: 2018/12/12 - 18:00
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Performance Improvement Opportunities with NUMA Hardware

Intel’s non-uniform memory access (NUMA) strategy is based on several new memory technologies that promise significant improvements in both capability and performance. This article provides information on Multi-Channel DRAM (MCDRAM) and High-Bandwidth Memory (HBM), Non-volatile dual inline-memory modules (NVDIMMs), and Intel® Omni-Path Fabric (Intel® OP Fabric).
作者: 最后更新时间: 2019/09/30 - 17:30
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Modern Memory Subsystems Benefits for Data Base Codes, Linear Algebra Codes, Big Data, and Enterprise Storage

This article describes and contrasts advantages different types of memory, including Multi-Channel DRAM (MCDRAM) and High-Bandwidth Memory (HBM), the future 3D XPoint™ memory devices, and Intel® Omni-Path Fabric (Intel® OP Fabric).
作者: 最后更新时间: 2019/09/30 - 17:28
Article

Improve Performance with Vectorization

This article focuses on the steps to improve software performance with vectorization. Included are examples of full applications along with some simpler cases to illustrate the steps to vectorization.
作者: David M. 最后更新时间: 2019/10/15 - 15:30
Article

Putting Your Data and Code in Order: Data and layout - Part 2

Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
作者: David M. 最后更新时间: 2019/10/15 - 16:40
Article

整理您的数据和代码: 数据和布局 - 第 2 部分

Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
作者: David M. 最后更新时间: 2019/10/15 - 16:40