Hands-On GPU Programming with Python and CUDA
Explore high-performance parallel computing with CUDA
1st Edition
Published on 3. June 2024
310 pages
978-1-78899-522-1 (ISBN)
Description
No detailed description available for "Hands-On GPU Programming with Python and CUDA".
More details
Other editions
Additional editions
Dr. Brian Tuomanen
Hands-On GPU Programming with Python and CUDA
Explore high-performance parallel computing with CUDA
Book
11/2018
Packt Publishing
€56.20
Shipment within 3-4 weeks
Person
Tuomanen Dr. Brian :
Dr. Brian Tuomanen has been working with CUDA and General-Purpose GPU Programming since 2014. He received his Bachelor of Science in Electrical Engineering from the University of Washington in Seattle, and briefly worked as a Software Engineer before switching to Mathematics for Graduate School. He completed his Ph.D. in Mathematics at the University of Missouri in Columbia, where he first encountered GPU programming as a means for studying scientific problems. Dr. Tuomanen has spoken at the US Army Research Lab about General Purpose GPU programming, and has recently lead GPU integration and development at a Maryland based start-up company. He currently lives and works in the Seattle area.
Dr. Brian Tuomanen has been working with CUDA and General-Purpose GPU Programming since 2014. He received his Bachelor of Science in Electrical Engineering from the University of Washington in Seattle, and briefly worked as a Software Engineer before switching to Mathematics for Graduate School. He completed his Ph.D. in Mathematics at the University of Missouri in Columbia, where he first encountered GPU programming as a means for studying scientific problems. Dr. Tuomanen has spoken at the US Army Research Lab about General Purpose GPU programming, and has recently lead GPU integration and development at a Maryland based start-up company. He currently lives and works in the Seattle area.
Content
Table of Contents - Why GPU Programming?
- Setting Up Your GPU Programming Environment
- Getting Started with PyCUDA
- Kernels, Threads, Blocks, and Grids
- Streams, Events, Contexts, and Concurrency
- Debugging and Profiling Your CUDA Code
- Using the CUDA Libraries with Scikit-CUDA Draft complete
- The CUDA Device Function Libraries and Thrust
- Implementing a Deep Neural Network
- Working with Compiled GPU Code
- Performance Optimization in CUDA
- Where to Go from Here
- Setting Up Your GPU Programming Environment
- Getting Started with PyCUDA
- Kernels, Threads, Blocks, and Grids
- Streams, Events, Contexts, and Concurrency
- Debugging and Profiling Your CUDA Code
- Using the CUDA Libraries with Scikit-CUDA Draft complete
- The CUDA Device Function Libraries and Thrust
- Implementing a Deep Neural Network
- Working with Compiled GPU Code
- Performance Optimization in CUDA
- Where to Go from Here
