42,514 research outputs found
The Folklore of Sorting Algorithms
The objective of this paper is to review the folklore knowledge seen in research work devoted on synthesis, optimization, and effectiveness of various sorting algorithms. We will examine sorting algorithms in the folklore lines and try to discover the tradeoffs between folklore and theorems. Finally, the folklore knowledge on complexity values of the sorting algorithms will be considered, verified and subsequently converged in to theorems
An empirical evaluation of High-Level Synthesis languages and tools for database acceleration
High Level Synthesis (HLS) languages and tools are emerging as the most promising technique to make FPGAs more accessible to software developers. Nevertheless, picking the most suitable HLS for a certain class of algorithms depends on requirements such as area and throughput, as well as on programmer experience. In this paper, we explore the different trade-offs present when using a representative set of HLS tools in the context of Database Management Systems (DBMS) acceleration. More specifically, we conduct an empirical analysis of four representative frameworks (Bluespec SystemVerilog, Altera OpenCL, LegUp and Chisel) that we utilize to accelerate commonly-used database algorithms such as sorting, the median operator, and hash joins. Through our implementation experience and empirical results for database acceleration, we conclude that the selection of the most suitable HLS depends on a set of orthogonal characteristics, which we highlight for each HLS framework.Peer ReviewedPostprint (author’s final draft
Synthesis of Parametric Programs using Genetic Programming and Model Checking
Formal methods apply algorithms based on mathematical principles to enhance
the reliability of systems. It would only be natural to try to progress from
verification, model checking or testing a system against its formal
specification into constructing it automatically. Classical algorithmic
synthesis theory provides interesting algorithms but also alarming high
complexity and undecidability results. The use of genetic programming, in
combination with model checking and testing, provides a powerful heuristic to
synthesize programs. The method is not completely automatic, as it is fine
tuned by a user that sets up the specification and parameters. It also does not
guarantee to always succeed and converge towards a solution that satisfies all
the required properties. However, we applied it successfully on quite
nontrivial examples and managed to find solutions to hard programming
challenges, as well as to improve and to correct code. We describe here several
versions of our method for synthesizing sequential and concurrent systems.Comment: In Proceedings INFINITY 2013, arXiv:1402.661
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