In Association with
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| Date: |
June 4-7, 2012 |
| Location: |
One Memorial Drive
Cambridge
MA 02142 |
| Contact: |
Acceleware
403.249.9099 x 356, services@acceleware.com |
| Cost: |
$3250 USD |
This 4 day CUDA training course has a life science theme and the hands-on exercise you will be working on for day 4 features an application of CUDA in the field. Commonly used algorithms such as Monte Carlo methods, FFT and filtering will be used and profiled in examples. The case study on day 4 focuses on the efficient implementation of a molecular dynamics simulation. A background in life science is not necessary.
Your fees include:
- Use of a laptop equipped with CUDA capable GPU
- Manual of all lectures
- Electronic copy of lab exercises
- Certificate of Completion
- Morning beverages (coffee, tea, juices) and afternoon snacks
Space is limited - Please register early to guarantee your spot
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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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Your Trainer
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Michael is a software developer for the Reverse Time Migration product at Acceleware. He has been responsible for evaluating new platforms that can offer significant speedups due to their parallel architectures. His previous experience includes building systems for parallel and distributed 3D video rendering.
Michael has a Masters in Physics from University of Calgary and a Bachelors in Computer Science from Université du Québec à Trois-Rivières.
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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: Molecular Dynamics Simulation Case Study
- Lecture: Profiling CUDA Applications
- Hands-on-Exercise: Profiling CUDA 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
