In Association with
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| Date: |
May 10th - 13th, 2011 |
| Location: |
1065 La Avenida
Mountain View, CA 94043
650-693-1001
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| Contact: |
Acceleware
403.249.9099 x 356, services@acceleware.com |
| Cost: |
$3250 USD
Your fee includes:
- Use of a laptop equipped with CUDA capable GPU
- Manual of all lectures
- CD copy of lab exercises
- Certificate of Completion
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| Coupon Code: |
AXTEB2011 (Register by May 2nd, 2011 with this coupon code and receive a $250 discount!) |
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Space is limited - Please register early to guarantee your spot.
Your Instructor
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Chris Mason - Acceleware Product Manager
Chris is the product manager for the linear algebra solver product line at Acceleware. He has been responsible for the successful launch of Acceleware products used by companies world-wide. His previous experience includes parallelization of algorithms on digital signal processors (DSPs) for cellular phones and base stations.
Chris has a Masters in Electrical Engineering from Stanford University. |
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For our hands-on exercises students will be working with Microsoft Visual Studio™ and NVIDIA® Parallel Nsight™.
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Schedule
Tue-Fri: 9:00AM – 5:00PM (incl. 1 hour lunch)
Agenda
- Day 1:
- Lecture: Overview of GPU Computing
- Hands-on-Exercise: Memory Allocation and Memory Transfers
- Lecture: Data-Parallel Architectures and the CUDA Programming Model
- Hands-on-Exercise: Simple Kernels
- Lecture: The CUDA Memory Model & Thread Cooperation
- Hands-on-Exercise: Shared Memory and Constant Memory
- Day 2:
- Lecture: Textures
- Hands-on-Exercise: Textures
- Lecture: Asynchronous Operations
- Hands-on-Exercise: Asynchronous Operations
- Lecture: Other GPU Features
- Lecture: CUDA Libraries
- Hands-on-Exercise: CUDA Libraries
- Day 3:
- Lecture: Debugging Tools and Techniques
- Hands-on-Exercise: Debugging Tools and Techniques
- Lecture: Introduction to Optimization
- Hands-on-Exercise: Arithmetic Optimization
- Lecture: Resource Management, Latency and Occupancy
- Hands-on-Exercise: Occupancy Calculator
- Day 4 :
- Lecture: Memory Performance Optimizations
- Hands-on-Exercise: Memory Performance Optimizations
- Lecture: Profiling CUDA/OpenCL Applications
- Hands-on-Exercise: Profiling CUDA/OpenCL Applications
- Lecture: Driver API
- Hands-on-Exercise: Driver API
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All lectures are a combination of teaching and hands-on tutorials
NVIDIA’s foundational training material is augmented with Acceleware’s experience over
the past 7 years and with examples specific to an HPC audience
