CERN Accelerating science

Talk
Title GOoDA: The Generic Optimization Data Analyzer
Video
Loading
If you experience any problem watching the video, click the download button below
Download Embed
Mp4:High
(600 kbps)
Windows Media:Medium
(480 kbps)
Flash:High
(753 kbps)
High-resolution:
Copy-paste this code into your page:
Author(s) Vitillo, Roberto Agostino (speaker) (LBNL)
Corporate author(s) CERN. Geneva
Imprint 2012-05-21. - Streaming video, 00:23:43:00.
Series (Conferences)
(Computing in High Energy and Nuclear Physics (CHEP) 2012)
Lecture note on 2012-05-21T13:55:00
Subject category Conferences
Abstract Modern superscalar, out-of-order microprocessors dominate large scale server computing. Monitoring their activity, during program execution, has become complicated due to the complexity of the microarchitectures and their IO interactions. Recent processors have thousands of performance monitoring events. These are required to actually provide coverage for all of the complex interactions and performance issues that can occur. Knowing which data to collect and how to interpret the results has become an unreasonable burden for code developers whose tasks are already hard enough. It becomes the task of the analysis tool developer to bridge this gap. To address this issue, a generic decomposition of how a microprocessor is using the consumed cycles allows code developers to quickly understand which of the myriad of microarchitectural complexities they are battling, without requiring a detailed knowledge of the microarchitecture. When this approach is intrinsically integrated into a performance data analysis tool, it enables software developers to take advantage of the microarchitectural methodology that has only been available to experts. The Generic Optimization Data Analyzer (GOoDA) project integrates this expertise into a profiling tool in order to lower the required expertise of the user and, being designed from the ground up with large-scale object-oriented applications in mind, it will be particularly useful for large HENP codebases
Copyright/License © 2012-2024 CERN
Submitted by jd@bnl.gov

 


 Record created 2012-07-09, last modified 2022-11-02


External links:
Download fulltextTalk details
Download fulltextEvent details