43,343 research outputs found
Towards an Achievable Performance for the Loop Nests
Numerous code optimization techniques, including loop nest optimizations,
have been developed over the last four decades. Loop optimization techniques
transform loop nests to improve the performance of the code on a target
architecture, including exposing parallelism. Finding and evaluating an
optimal, semantic-preserving sequence of transformations is a complex problem.
The sequence is guided using heuristics and/or analytical models and there is
no way of knowing how close it gets to optimal performance or if there is any
headroom for improvement. This paper makes two contributions. First, it uses a
comparative analysis of loop optimizations/transformations across multiple
compilers to determine how much headroom may exist for each compiler. And
second, it presents an approach to characterize the loop nests based on their
hardware performance counter values and a Machine Learning approach that
predicts which compiler will generate the fastest code for a loop nest. The
prediction is made for both auto-vectorized, serial compilation and for
auto-parallelization. The results show that the headroom for state-of-the-art
compilers ranges from 1.10x to 1.42x for the serial code and from 1.30x to
1.71x for the auto-parallelized code. These results are based on the Machine
Learning predictions.Comment: Accepted at the 31st International Workshop on Languages and
Compilers for Parallel Computing (LCPC 2018
AsmetaF: A Flattener for the ASMETA Framework
Abstract State Machines (ASMs) have shown to be a suitable high-level
specification method for complex, even industrial, systems; the ASMETA
framework, supporting several validation and verification activities on ASM
models, is an example of a formal integrated development environment. Although
ASMs allow modeling complex systems in a rather concise way -and this is
advantageous for specification purposes-, such concise notation is in general a
problem for verification activities as model checking and theorem proving that
rely on tools accepting simpler notations.
In this paper, we propose a flattener tool integrated in the ASMETA framework
that transforms a general ASM model in a flattened model constituted only of
update, parallel, and conditional rules; such model is easier to map to
notations of verification tools. Experiments show the effect of applying the
tool to some representative case studies of the ASMETA repository.Comment: In Proceedings F-IDE 2018, arXiv:1811.09014. The first two authors
are supported by ERATO HASUO Metamathematics for Systems Design Project (No.
JPMJER1603), JST. Funding Reference number: 10.13039/501100009024 ERAT
Automatic program generation from specifications using PROLOG
An automatic program generator which creates PROLOG programs from input/output specifications is described. The generator takes as input descriptions of the input and output data types, a set of transformations and the input/output relation. Abstract data types are used as models for data. They are defined as sets of terms satisfying a system of equations. The tests, the transformations and the input/output relation are also specified by equations
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