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

RoadRunneR: A Small And Fast Bitslice Block Cipher For Low Cost 8-bit Processors

Designing block ciphers targeting resource constrained 8-bit CPUs is a challenging problem. There are many recent lightweight ciphers designed for better performance in hardware. On the other hand, most software efficient lightweight ciphers either lack a security proof or have a low security margin. To fill the gap, we present RoadRunneR which is an efficient block cipher in 8-bit software, and its security is provable against differential and linear attacks. RoadRunneR has lowest code size in Atmel’s ATtiny45, except NSA’s design SPECK, which has no security proof. Moreover, we propose a new metric for the fair comparison of block ciphers. This metric, called ST/A, is the first metric to use key length as a parameter to rank ciphers of different key length in a fair way. By using ST/A and other metrics in the literature, we show that RoadRunneR is competitive among existing ciphers on ATtiny45

A runtime heuristic to selectively replicate tasks for application-specific reliability targets

In this paper we propose a runtime-based selective task replication technique for task-parallel high performance computing applications. Our selective task replication technique is automatic and does not require modification/recompilation of OS, compiler or application code. Our heuristic, we call App_FIT, selects tasks to replicate such that the specified reliability target for an application is achieved. In our experimental evaluation, we show that App FIT selective replication heuristic is low-overhead and highly scalable. In addition, results indicate that complete task replication is overkill for achieving reliability targets. We show that with App FIT, we can tolerate pessimistic exascale error rates with only 53% of the tasks being replicated.This work was supported by FI-DGR 2013 scholarship and the European Community’s Seventh Framework Programme [FP7/2007-2013] under the Mont-blanc 2 Project (www.montblanc-project.eu), grant agreement no. 610402 and in part by the European Union (FEDER funds) under contract TIN2015-65316-P.Peer ReviewedPostprint (author's final draft

Systematization of a 256-bit lightweight block cipher Marvin

In a world heavily loaded by information, there is a great need for keeping specific information secure from adversaries. The rapid growth in the research field of lightweight cryptography can be seen from the list of the number of lightweight stream as well as block ciphers that has been proposed in the recent years. This paper focuses only on the subject of lightweight block ciphers. In this paper, we have proposed a new 256 bit lightweight block cipher named as Marvin, that belongs to the family of Extended LS designs.Comment: 12 pages,6 figure

A MAC Mode for Lightweight Block Ciphers

status: accepte

Triathlon of Lightweight Block Ciphers for the Internet of Things

In this paper, we introduce a framework for the benchmarking of lightweight block ciphers on a multitude of embedded platforms. Our framework is able to evaluate the execution time, RAM footprint, as well as binary code size, and allows one to define a custom "figure of merit" according to which all evaluated candidates can be ranked. We used the framework to benchmark implementations of 19 lightweight ciphers, namely AES, Chaskey, Fantomas, HIGHT, LBlock, LEA, LED, Piccolo, PRESENT, PRIDE, PRINCE, RC5, RECTANGLE, RoadRunneR, Robin, Simon, SPARX, Speck, and TWINE, on three microcontroller platforms: 8-bit AVR, 16-bit MSP430, and 32-bit ARM. Our results bring some new insights into the question of how well these lightweight ciphers are suited to secure the Internet of things. The benchmarking framework provides cipher designers with an easy-to-use tool to compare new algorithms with the state of the art and allows standardization organizations to conduct a fair and consistent evaluation of a large number of candidates