Home ›

Parallel Programming with NVIDIA CUDA

Issue 200

From Issue #200
December 2010

Feb 01, 2011  By Alejandro Segovia
 in
Using hardware acceleration via General Programming on stock GPUs (GPGPU), I've sped up my algorithms by more than tenfold. This article shows how you can achieve these results too!
Conclusion

If parallelization of your algorithm is possible, using CUDA will speed up your computations dramatically, allowing you to make the most out of your hardware.

The main challenge consists in deciding how to partition your problem into chunks suitable for parallel execution. As with so many other aspects in parallel programming, this is where experience and—why not—imagination come into play.

Additional techniques offer room for even more improvement. In particular, the on-chip shared memory of each compute node allows further speedup of the computation process.

Resources

Hardware-Accelerated Computation: gpgpu.org

Source Code and Video: www.coroware.com/streamprocessing

Getting Started on CUDA: www.nvidia.com/cuda

ATI-Based Stream SDK: developer.ati.com/gpu/atistreamsdk

OpenCL Official Web Site: www.khronos.org/opencl

Alejandro Segovia is a parallel programming advisor for CoroWare. He is also a contributing partner at RealityFrontier. He works in 3-D graphic development and GPU acceleration. Alejandro was recently a visiting scientist at the University of Delaware where he investigated CUDA from an academic standpoint. His findings were published at the IEEE IPCCC Conference in 2009.

______________________

Comments

Comment viewing options

Select your preferred way to display the comments and click "Save settings" to activate your changes.

The statement minima[y][x] =

Anonymous's picture
Submitted by Anonymous (not verified) on Wed, 03/02/2011 - 21:02.

The statement
minima[y][x] = (norm(field[y][x]) < threshold) ? true : false
may incur branching penalty

You can just use the first part
minima[y][x] = (norm(field[y][x]) < threshold)

Webcast
More Resources
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers

Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.

Learn More

Sponsored by AMD

White Paper
More Resources
Private PaaS for the Agile Enterprise

If you already use virtualized infrastructure, you are well on your way to leveraging the power of the cloud. Virtualization offers the promise of limitless resources, but how do you manage that scalability when your DevOps team doesn’t scale? In today’s hypercompetitive markets, fast results can make a difference between leading the pack vs. obsolescence. Organizations need more benefits from cloud computing than just raw resources. They need agility, flexibility, convenience, ROI, and control.

Stackato private Platform-as-a-Service technology from ActiveState extends your private cloud infrastructure by creating a private PaaS to provide on-demand availability, flexibility, control, and ultimately, faster time-to-market for your enterprise.

Learn More

Sponsored by ActiveState