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Tuning IBM System x Servers for Performance
Tuning IBM System x Servers for Performance

Beowulf Cluster Computing with Linux (Scientific and Engineering
Beowulf Cluster Computing with Linux (Scientific and Engineering

Tuning IBM System x Servers for Performance
Tuning IBM System x Servers for Performance

... 6.3.5 IBM CPU passthru card . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 6.4 64-bit computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 6.5 Processor performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Embedded Linux system development Embedded
Embedded Linux system development Embedded

Dept. of CSE, BUAA
Dept. of CSE, BUAA

... Robustness is the child of transparency and simplicity. Design for simplicity; add complexity only where you must. Design for transparency; spend effort early to save effort later. In interface design, obey the Rule of Least Surprise. Programmer time is expensive; conserve it in preference to m ...
Windows Embedded CE 6.0 MCTS Exam Preparation Kit
Windows Embedded CE 6.0 MCTS Exam Preparation Kit

... represents the current view of Microsoft Corporation on the issues discussed as of the date of publication. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any infor ...
Machine-Level Programming I: Basics
Machine-Level Programming I: Basics

An Evaluation of Soft Processors as a Reliable Computing Platform
An Evaluation of Soft Processors as a Reliable Computing Platform

PDF
PDF

Chapter 3
Chapter 3

... processing the request and generating the response are both handled by a single servlet class ...
Using Standard Operating Systems for Time Critical Applications
Using Standard Operating Systems for Time Critical Applications

Rethinking host network stack architecture using a
Rethinking host network stack architecture using a

Optimization Techniques for Adaptability in MPI Applications
Optimization Techniques for Adaptability in MPI Applications

Threads Threads, User vs. Kernel Threads, Java Threads, Threads
Threads Threads, User vs. Kernel Threads, Java Threads, Threads

... Question: what will the output be? Answer: Impossible to tell for sure If you know the implementation of the JVM on your particular machine, then you may be able to tell  But if you write this code to be run anywhere, then you can’t expect to know what happens ...
PS14
PS14

... Imperative Programming Evaluator: Adding while expressions Question1: Can a while expression be supported in a functional programming evaluator? NO! in functional programming there is no change of state. Particularly, the result of evaluating the condition clause of a while loop will always remain ...
NetXpress IP Multiplexer
NetXpress IP Multiplexer

Survey of Cyber Moving Targets Lincoln Laboratory Technical Report 1166
Survey of Cyber Moving Targets Lincoln Laboratory Technical Report 1166

TCP/UDP Sockets
TCP/UDP Sockets

here
here

Clustered Objects - Computer Science
Clustered Objects - Computer Science

... Gamsa observes that, prior to addressing specific data structures and algorithms of the operating system in the small, a more fundamental restructuring of the operating system can reduce the impact of sharing induced by the workload by minimizing sharing in the operating system structure [49]. Gamsa ...
Cluster Computing: Architectures, Operating Systems, Parallel
Cluster Computing: Architectures, Operating Systems, Parallel

Nachos Introduction
Nachos Introduction

TCP/UDP Sockets
TCP/UDP Sockets

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Socket Programming

...  listen is non-blocking: returns immediately  int s = accept(sock, &name, &namelen);  s: integer, the new socket (used for data-transfer)  sock: integer, the orig. socket (being listened on)  name: struct sockaddr, address of the active participant ...
Lecture slides for Chapter 9
Lecture slides for Chapter 9

1 2 3 4 5 ... 23 >

Stream processing

Stream processing is a computer programming paradigm, equivalent to data-flow programming and reactive programming, that allows some applications to more easily exploit a limited form of parallel processing. Such applications can use multiple computational units, such as the FPUs on a GPU or field programmable gate arrays (FPGAs), without explicitly managing allocation, synchronization, or communication among those units.The stream processing paradigm simplifies parallel software and hardware by restricting the parallel computation that can be performed. Given a set of data (a stream), a series of operations (kernel functions) is applied to each element in the stream. Uniform streaming, where one kernel function is applied to all elements in the stream, is typical. Kernel functions are usually pipelined, and local on-chip memory is reused to minimize external memory bandwidth. Since the kernel and stream abstractions expose data dependencies, compiler tools can fully automate and optimize on-chip management tasks. Stream processing hardware can use scoreboarding, for example, to launch DMAs at runtime, when dependencies become known. The elimination of manual DMA management reduces software complexity, and the elimination of hardware caches reduces the amount of the area not dedicated to computational units such as ALUs.During the 1980s stream processing was explored within dataflow programming. An example is the language SISAL (Streams and Iteration in a Single Assignment Language).
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