3,033 research outputs found
Continuous Performance Benchmarking Framework for ROOT
Foundational software libraries such as ROOT are under intense pressure to
avoid software regression, including performance regressions. Continuous
performance benchmarking, as a part of continuous integration and other code
quality testing, is an industry best-practice to understand how the performance
of a software product evolves over time. We present a framework, built from
industry best practices and tools, to help to understand ROOT code performance
and monitor the efficiency of the code for a several processor architectures.
It additionally allows historical performance measurements for ROOT I/O,
vectorization and parallelization sub-systems.Comment: 8 pages, 5 figures, CHEP 2018 - 23rd International Conference on
Computing in High Energy and Nuclear Physic
Reviewer Integration and Performance Measurement for Malware Detection
We present and evaluate a large-scale malware detection system integrating
machine learning with expert reviewers, treating reviewers as a limited
labeling resource. We demonstrate that even in small numbers, reviewers can
vastly improve the system's ability to keep pace with evolving threats. We
conduct our evaluation on a sample of VirusTotal submissions spanning 2.5 years
and containing 1.1 million binaries with 778GB of raw feature data. Without
reviewer assistance, we achieve 72% detection at a 0.5% false positive rate,
performing comparable to the best vendors on VirusTotal. Given a budget of 80
accurate reviews daily, we improve detection to 89% and are able to detect 42%
of malicious binaries undetected upon initial submission to VirusTotal.
Additionally, we identify a previously unnoticed temporal inconsistency in the
labeling of training datasets. We compare the impact of training labels
obtained at the same time training data is first seen with training labels
obtained months later. We find that using training labels obtained well after
samples appear, and thus unavailable in practice for current training data,
inflates measured detection by almost 20 percentage points. We release our
cluster-based implementation, as well as a list of all hashes in our evaluation
and 3% of our entire dataset.Comment: 20 papers, 11 figures, accepted at the 13th Conference on Detection
of Intrusions and Malware & Vulnerability Assessment (DIMVA 2016
New Developments in FormCalc 8.4
We present new developments in FeynArts 3.9 and FormCalc 8.4, in particular
the MSSMCT model file including the complete one-loop renormalization,
vectorization/parallelization issues, and the interface to the Ninja library
for tensor reduction.Comment: 7 pages, proceedings contribution to Loops & Legs 2014, April 27-May
2, 2014, Weimar, German
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