MILEPOST GCC: machine learning based research compiler

International audienceTuning hardwired compiler optimizations for rapidly evolving hardware makes porting an optimizing compiler for each new platform extremely challenging. Our radical approach is to develop a modular, extensible, self-optimizing compiler that automatically learns the best optimization heuristics based on the behavior of the platform. In this paper we describe MILEPOST GCC, a machine-learning-based compiler that automatically adjusts its optimization heuristics to improve the execution time, code size, or compilation time of specific programs on different architectures. Our preliminary experimental results show that it is possible to considerably reduce execution time of the MiBench benchmark suite on a range of platforms entirely automatically

Milepost GCC: Machine Learning Enabled Self-tuning Compiler

International audienceTuning compiler optimizations for rapidly evolving hardwaremakes porting and extending an optimizing compiler for each new platform extremely challenging. Iterative optimization is a popular approach to adapting programs to a new architecture automatically using feedback-directed compilation. However, the large number of evaluations required for each program has prevented iterative compilation from widespread take-up in production compilers. Machine learning has been proposed to tune optimizations across programs systematically but is currently limited to a few transformations, long training phases and critically lacks publicly released, stable tools. Our approach is to develop a modular, extensible, self-tuning optimization infrastructure to automatically learn the best optimizations across multiple programs and architectures based on the correlation between program features, run-time behavior and optimizations. In this paper we describeMilepostGCC, the first publicly-available open-source machine learning-based compiler. It consists of an Interactive Compilation Interface (ICI) and plugins to extract program features and exchange optimization data with the cTuning.org open public repository. It automatically adapts the internal optimization heuristic at function-level granularity to improve execution time, code size and compilation time of a new program on a given architecture. Part of the MILEPOST technology together with low-level ICI-inspired plugin framework is now included in the mainline GCC.We developed machine learning plugins based on probabilistic and transductive approaches to predict good combinations of optimizations. Our preliminary experimental results show that it is possible to automatically reduce the execution time of individual MiBench programs, some by more than a factor of 2, while also improving compilation time and code size. On average we are able to reduce the execution time of the MiBench benchmark suite by 11% for the ARC reconfigurable processor.We also present a realistic multi-objective optimization scenario for Berkeley DB library using Milepost GCC and improve execution time by approximately 17%, while reducing compilatio

Mapping of algorithms onto programmable data-driven arrays

Control-driven arrays (e.g., systolic arrays) provide high levels of parallelism and pipelining for inherently regular computations. Data-driven arrays can provide the same for algorithms with no internal regularity. The purpose of this research is to establish a method for speeding up an algorithm by mapping and executing it on a data driven array. The array being used is an homogeneous, hexagonal, data driven processor array. Mapping a general algorithm, given in the data flow language SISAL, consists of translating the algorithm to a data flow graph (DFG) and assigning every node in the DFG to a processing element (PE) in the array. This research aims to find an efficient mapping that minimizes the area, and maximizes the performance of the given algorithm or find a tradeoff between the two

Molecular Serotyping of \u3ci\u3eEscherichia coli\u3c/i\u3e O26:H11

Serotyping is the foundation of pathogenic E. coli diagnostics; however, few laboratories have this capacity. We developed a molecular serotyping protocol that targets, genetically, the same somatic and flagellar antigens of E. coli O26:H11 used in traditional serotyping. It correctly serotypes strains untypeable by traditional methods, affording primary laboratories serotyping capabilities

MILEPOST GCC: machine learning based research compiler

Tuning hardwired compiler optimizations for rapidly evolving hardware makes porting an optimizing compiler for each new platform extremely challenging. Our radical approach is to develop a modular, extensible, self-optimizing compiler that automatically learns the best optimization heuristics based on the behavior of the platform. In this paper we describe MILEPOST GCC, a machine-learning-based compiler that automatically adjusts its optimization heuristics to improve the execution time, code size, or compilation time of specific programs on different architectures. Our preliminary experimental results show that it is possible to considerably reduce execution time of the MiBench benchmark suite on a range of platforms entirely automatically