世界是平行的：技术- X使得GPU的处理无障碍

谁做的研究人员计算密集的工作很多可以利用更多的处理能力。 Many of them actually have that power available on their computers, but haven't found a way to take advantage of it.

许多

其中许多实际上，权力在其计算机上可用，但还没有找到一种方法，利用它。 The computational clout is in the multiple processor cores of the computer's graphics system, where it is not easily accessible.计算的影响力就是它就不容易在计算机的图形系统，多个处理器核心。

A tool like NVIDIA's CUDA parallel computing model makes the GPU cores, up to 240 of them on the latest NVIDIA Tesla GPUs, available to programs.像NVIDIA的CUDA技术的并行计算模型的工具，使GPU核心，多达240对，其中最新的NVIDIA Tesla GPU的，提供给程序。 But to take maximum advantage of it, you have to be a skilled C or C++ programmer.但要掌握它最大的优势，你必须熟练的C或C + +程序员。 The problem is that many of the people who would benefit most from high-performance computing are not software developers by profession.问题是，谁的人将受益于高性能计算最不被许多专业软件开发人员。 They write customized code out of necessity, but their primary work is in chemistry, geology, astronomy, physics or biology.他们编写定制的代码出于需要，但他们的主要工作，化学，地质学，天文学，物理学和生物学的。

Tech-X Corp. , a Boulder, CO, software and consulting company specializing in high-performance scientific computing, is working to change that. 技术- X公司 ，一博尔德，二氧化碳，软件和咨询公司在高性能科学计算专业，正在努力改变这种状况。 Its GPUlib is a tool that brings GPU-based computing into the high-level tools used by researchers, including ITT Visual Information Solutions' IDL , Mathworks' MATLAB , and that trusty old laboratory standby, Fortran.它GPUlib是一种工具，使基于GPU进入高层次的研究人员使用的工具，包括ITT公司视觉信息解决方案'IDL中 ，Mathworks的MATLAB的 ，和电脑的可信任的老实验室待机，Fortran语言。

“Parallel computing used to be a very elite field,” says Peter Messmer, vice president for space applications at Tech-X. “并行计算原本是一个非常精英领域，说：”彼得梅斯梅尔，副在空间应用技术，X先生。 “Few applications are designed to take advantage of it. “很少应用，旨在充分利用它。 GPU processing makes it much more mainstream.” Until GPU processing came along, the cheapest way to get very high performance in the lab was by building a cluster of relatively inexpensive PCs, but this took skills that researchers who weren't computer scientists or electrical engineers often lacked. GPU的处理能力使得它更成为主流。Until GPU的处理“出现了，最便宜的方式得到很高的实验室里的表现，通过建立一种相对廉价的电脑联网，但这个技能，研究人员采取谁没有计算机科学家或电工程师往往缺乏。 “The GPU makes it much more mainstream,” says Messmer. “该处理器使更多的主流，说：”梅斯麦。

GPU cores are best at vector processing , math in which large arrays of data are manipulated simultaneously, since that is what is needed for the GPU's primary task of rendering graphics. GPU核心是在矢量处理最好，数学中的大量数据数组操纵同时，因为这正是对GPU的图形呈现的首要任务需要。 That made Tech-X's choice of working with IDL and MATLAB a natural, since these tools are already optimized for manipulating vector data.这使科技- X的与IDL和MATLAB的一种自然的工作选择，因为这些工具已用于操作矢量数据进行了优化。 Typical uses include image processing for astronomical and remote sensing data and medical imaging.典型的应用包括天文图像和遥感数据和医疗影像处理。

Messmer says a major challenge is just getting researchers to try GPU computing.梅斯梅尔说，一个主要的挑战就是怎样让科研人员尝试GPU计算。 “People have heard a lot about GPU computing, but they are skeptical,” he says. “人们听到了很多关于GPU计算，但他们怀疑，”他说。 “They remember the field-programmable gate array hype from a few years ago, where it turned out to be too complicated for people to do anything. “他们记得几年前，那里原来的现场可编程门阵列炒作太复杂，人们做任何事情。 GPUlib helps because it maps to how people already think about problems.” GPUlib帮助，因为它映射到人们已经对问题的看法。“

GPUlib is free for academic use and $495 for commercial use. GPUlib是免费的学术用途，并为495美元用于商业用途。 It is available for Windows, Mac, and Linux.它可以在Windows，Mac和Linux操作系统。

This post is an entry in The World Isn't Flat, It's Parallel series running on nTersect, focused on the GPU's importance and the future of parallel processing. 这个职位是在世界进入不是平坦的，它的并行运行nTersect系列，在GPU的重要性和并行处理着眼未来。 Today, GPUs can operate faster and more cost-efficiently than CPUs in a range of increasingly important sectors, such as medicine, national security, natural resources and emergency services. 今天，GPU可以运行在一个日益重要的行业 ， 如医药，更快 ， 更广泛成本比CPU的高效地，国家安全，自然资源和应急服务。 For more information on GPUs and their applications, keep your eyes on The World Isn't Flat, It's Parallel . 欲了解更多有关图形及其应用的信息，不断的世界你的眼睛不是平的，它的并行 。

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