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Reliability for general purpose processing on the GPU (GPGPU) is becoming a weak link in the construction of reliable supercomputer systems. Because hardware protection is expensive to develop, requires dedicated on-chip resources, and is not portable across different architectures, the efficiency of software solutions such as redundant multithreading (RMT)(More)
The Levenshtein Nondeterministic Finite state Automaton (NFA) recognizes input strings within a set edit distance of a configured pattern in linear time. This automaton can be pipelined to recognize all substrings of an input text in linear time with additional use of nondeterminism. In general , von Neumann hardware cannot directly execute NFAs without(More)
Micron's new Automata Processor (AP) architecture exploits the very high and natural level of parallelism found in DRAM technologies to achieve native-hardware implementation of non-deterministic finite automata (NFAs). The use of DRAM technology to implement the NTA states provides high capacity and therefore provide extraordinary parallelism for pattern(More)
—High-performance automata-processing engines are traditionally evaluated using a limited set of regular expression-rulesets. While regular expression rulesets are valid real-world examples of use cases for automata processing, they represent a small proportion of all use cases for automata-based computing. With the recent availability of architectures and(More)
Micron's Automata Processor (AP) efficiently emulates non-deterministic finite automata and has been shown to provide large speedups over traditional von Neumann execution for massively parallel, rule-based, data-mining and pattern matching applications. We demonstrate the AP's ability to generate high-quality and energy efficient pseudo-random behavior for(More)
Brill tagging is a classic rule-based algorithm for part-of-speech (POS) tagging that assigns tags, such as nouns, verbs, adjectives, etc., to input tokens. Due to the the intense memory requirements of rule matching, CPU implementations of the Brill tagging algorithm have been found to be slow. We show that Micron's Automata Processor (AP) - a new(More)
Problem Many big data applications reduce to highly parallel pattern search problems against a single stream of data Hardware accelerators, such as the Automata Processor and FPGAs, support these searches, but programming can be challenging Goal: A concise, maintainable programming model that maximizes the number of parallel searches for higher throughput 6(More)
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