Parallel Programming with NVIDIA CUDA
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.
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.
- Three EU Industries That Need HPC Now
- Chemistry on the Desktop
- Five HPC Cost Considerations to Maximize ROI
- FinTech and SAP HANA
- HOSTING Monitoring Insights
- Preseeding Full Disk Encryption
- William Rothwell and Nick Garner's Certified Ethical Hacker Complete Video Course (Pearson IT Certification)
- Two Factors Are Better Than One
- GRUB Boot from ISO
- Two Ways GDPR Will Change Your Data Storage Solution