112,360 research outputs found
SmartTrack: Efficient Predictive Race Detection
Widely used data race detectors, including the state-of-the-art FastTrack
algorithm, incur performance costs that are acceptable for regular in-house
testing, but miss races detectable from the analyzed execution. Predictive
analyses detect more data races in an analyzed execution than FastTrack
detects, but at significantly higher performance cost.
This paper presents SmartTrack, an algorithm that optimizes predictive race
detection analyses, including two analyses from prior work and a new analysis
introduced in this paper. SmartTrack's algorithm incorporates two main
optimizations: (1) epoch and ownership optimizations from prior work, applied
to predictive analysis for the first time; and (2) novel conflicting critical
section optimizations introduced by this paper. Our evaluation shows that
SmartTrack achieves performance competitive with FastTrack-a qualitative
improvement in the state of the art for data race detection.Comment: Extended arXiv version of PLDI 2020 paper (adds Appendices A-E) #228
SmartTrack: Efficient Predictive Race Detectio
Efficient, Near Complete and Often Sound Hybrid Dynamic Data Race Prediction (extended version)
Dynamic data race prediction aims to identify races based on a single program
run represented by a trace. The challenge is to remain efficient while being as
sound and as complete as possible. Efficient means a linear run-time as
otherwise the method unlikely scales for real-world programs. We introduce an
efficient, near complete and often sound dynamic data race prediction method
that combines the lockset method with several improvements made in the area of
happens-before methods. By near complete we mean that the method is complete in
theory but for efficiency reasons the implementation applies some optimizations
that may result in incompleteness. The method can be shown to be sound for two
threads but is unsound in general. We provide extensive experimental data that
shows that our method works well in practice.Comment: typos, appendi
Dynamic Race Prediction in Linear Time
Writing reliable concurrent software remains a huge challenge for today's
programmers. Programmers rarely reason about their code by explicitly
considering different possible inter-leavings of its execution. We consider the
problem of detecting data races from individual executions in a sound manner.
The classical approach to solving this problem has been to use Lamport's
happens-before (HB) relation. Until now HB remains the only approach that runs
in linear time. Previous efforts in improving over HB such as causally-precedes
(CP) and maximal causal models fall short due to the fact that they are not
implementable efficiently and hence have to compromise on their race detecting
ability by limiting their techniques to bounded sized fragments of the
execution. We present a new relation weak-causally-precedes (WCP) that is
provably better than CP in terms of being able to detect more races, while
still remaining sound. Moreover it admits a linear time algorithm which works
on the entire execution without having to fragment it.Comment: 22 pages, 8 figures, 1 algorithm, 1 tabl
Spike sorting for large, dense electrode arrays
Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%
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