Is dynamic compilation possible for embedded systems ?

International audienceJIT compilation and dynamic compilation are powerful techniques allowing to delay the final code generation to the run-time. There is many benefits : improved portability, virtual machine security, etc. Unforturnately the tools used for JIT compilation and dynamic compilation does not met the classical requirement for embedded platforms: memory size is huge and code generation has big overheads. In this paper we show how dynamic code specialization (JIT) can be used and be beneficial in terms of execution speed and energy consumption with memory footprint kept under control. We based our approaches on our tool de-Goal and on LLVM, that we extended to be able to produce lightweight runtime specializers from annotated LLVM programs. Benchmarks are manipulated and transformed into templates and a specialization routine is build to instantiate the routines. Such approach allows to produce efficient special-izations routines, with a minimal energy consumption and memory footprint compare to a generic JIT application. Through some benchmarks, we present its efficiency in terms of speed, energy and memory footprint. We show that over static compilation we can achieve a speed-up of 21 % in terms of execution speed but also a 10 % energy reduction with a moderate memory footprint

Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox

This paper presents the Matlab package DeCo (Density Combination) which is based on the paper by Billio et al. (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights are time-varying and may depend on past predictive forecasting performances and other learning mechanisms. The core algorithm is the function DeCo which applies banks of parallel Sequential Monte Carlo algorithms to filter the time-varying combination weights. The DeCo procedure has been implemented both for standard CPU computing and for Graphical Process Unit (GPU) parallel computing. For the GPU implementation we use the Matlab parallel computing toolbox and show how to use General Purposes GPU computing almost effortless. This GPU implementation comes with a speed up of the execution time up to seventy times compared to a standard CPU Matlab implementation on a multicore CPU. We show the use of the package and the computational gain of the GPU version, through some simulation experiments and empirical application

Semantics-based analysis for optimizing compilation of concurrent programs

制度:新 ; 文部省報告番号:甲2127号 ; 学位の種類:博士(情報科学) ; 授与年月日:2005/12/22 ; 早大学位記番号:新412