1,165 research outputs found
Machine Learning for Microprocessor Performance Bug Localization
The validation process for microprocessors is a very complex task that
consumes substantial engineering time during the design process. Bugs that
degrade overall system performance, without affecting its functional
correctness, are particularly difficult to debug given the lack of a golden
reference for bug-free performance. This work introduces two automated
performance bug localization methodologies based on machine learning that aims
to aid the debugging process. Our results show that, the evaluated
microprocessor core performance bugs whose average IPC impact is greater than
1%, our best-performing technique is able to localize the exact
microarchitectural unit of the bug 77\% of the time, while achieving a
top-3 unit accuracy (out of 11 possible locations) of over 90% for bugs with
the same average IPC impact. The proposed system in our simulation setup
requires only a few seconds to perform a bug location inference, which leads to
a reduced debugging time.Comment: 12 pages, 6 figure
Understanding Persistent-Memory Related Issues in the Linux Kernel
Persistent memory (PM) technologies have inspired a wide range of PM-based
system optimizations. However, building correct PM-based systems is difficult
due to the unique characteristics of PM hardware. To better understand the
challenges as well as the opportunities to address them, this paper presents a
comprehensive study of PM-related issues in the Linux kernel. By analyzing
1,553 PM-related kernel patches in-depth and conducting experiments on
reproducibility and tool extension, we derive multiple insights in terms of PM
patch categories, PM bug patterns, consequences, fix strategies, triggering
conditions, and remedy solutions. We hope our results could contribute to the
development of robust PM-based storage systemsComment: ACM TRANSACTIONS ON STORAGE(TOS'23
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
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