Главная
Study mode:
on
1
Introduction
2
Outline
3
What is QMC
4
Goals
5
QMCPACK Code
6
Electron Count
7
GPU
8
Parallel Scalability
9
Main Operations
10
Mini QMC
11
Algorithmic Challenges
12
ASLA Approach
13
New Approach
14
Real World Results
15
Delayed Update
16
Development Approach
17
Version Control
18
Return on Investment
19
Real World Problems
20
Performance
21
Challenges
22
Conclusion
23
Questions
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the redesign and reimplementation of QMCPACK, a code for predicting material properties, in this 58-minute webinar from the Exascale Computing Project. Learn about development practices, extensive testing strategies, and approaches for achieving portability and performance on GPUs and CPUs. Gain insights into algorithmic challenges, the ASLA approach, and real-world results that can benefit HPC application developers and facilities. Discover how QMCPACK tackles electron count, GPU parallelization, and scaling issues. Understand the main operations, Mini QMC implementation, and the delayed update technique. Examine version control practices, return on investment considerations, and performance challenges faced during the development process.

Lessons Learned Developing Performance-Portable QMCPACK

Exascale Computing Project
Add to list