3,148 research outputs found
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
ACOTES project: Advanced compiler technologies for embedded streaming
Streaming applications are built of data-driven, computational components, consuming and producing unbounded data streams. Streaming oriented systems have become dominant in a wide range of domains, including embedded applications and DSPs. However, programming efficiently for streaming architectures is a challenging task, having to carefully partition the computation and map it to processes in a way that best matches the underlying streaming architecture, taking into account the distributed resources (memory, processing, real-time requirements) and communication overheads (processing and delay). These challenges have led to a number of suggested solutions, whose goal is to improve the programmer’s productivity in developing applications that process massive streams of data on programmable, parallel embedded architectures. StreamIt is one such example. Another more recent approach is that developed by the ACOTES project (Advanced Compiler Technologies for Embedded Streaming). The ACOTES approach for streaming applications consists of compiler-assisted mapping of streaming tasks to highly parallel systems in order to maximize cost-effectiveness, both in terms of energy and in terms of design effort. The analysis and transformation techniques automate large parts of the partitioning and mapping process, based on the properties of the application domain, on the quantitative information about the target systems, and on programmer directives. This paper presents the outcomes of the ACOTES project, a 3-year collaborative work of industrial (NXP, ST, IBM, Silicon Hive, NOKIA) and academic (UPC, INRIA, MINES ParisTech) partners, and advocates the use of Advanced Compiler Technologies that we developed to support Embedded Streaming.Peer ReviewedPostprint (published version
Iterative Compilation and Performance Prediction for Numerical Applications
Institute for Computing Systems ArchitectureAs the current rate of improvement in processor performance far exceeds the rate
of memory performance, memory latency is the dominant overhead in many
performance critical applications. In many cases, automatic compiler-based
approaches to improving memory performance are limited and programmers
frequently resort to manual optimisation techniques. However, this process is tedious
and time-consuming. Furthermore, a diverse range of a rapidly evolving hardware
makes the optimisation process even more complex. It is often hard to predict the
potential benefits from different optimisations and there are no simple criteria to stop
optimisations i.e. when optimal memory performance has been achieved or
sufficiently approached.
This thesis presents a platform independent optimisation approach for numerical
applications based on iterative feedback-directed program restructuring using a new
reasonably fast and accurate performance prediction technique for guiding
optimisations. New strategies for searching the optimisation space, by means of
profiling to find the best possible program variant, have been developed. These
strategies have been evaluated using a range of kernels and programs on different
platforms and operating systems. A significant performance improvement has been
achieved using new approaches when compared to the state-of-the-art native static
and platform-specific feedback directed compilers
Time for Reactive System Modeling
Reactive systems interact with their environment by reading inputs and computing and feeding back outputs in reactive cycles that are also called ticks. Often they are safety critical systems and are increasingly modeled with highlevel modeling tools. The concepts of the corresponding modeling languages are typically aimed to facilitate formal reasoning about program constructiveness to guarantee deterministic output and are explicitly abstracted from execution time aspects. Nevertheless, the worst-case execution time of a tick can be a crucial value, where exceedance can lead to lost inputs or tardy reaction to critical events. This thesis proposes a general approach to interactive timing analysis, which enables the feedback of detailed timing values directly in the model representation to support timing aware modeling. The concept is based on a generic timing interface that enables the exchangeability of the modeling as well as the timing analysis tool for the flexible implementation of varying tool chains. The proposed timing analysis approach includes visual highlighting and modeling pragmatics features to guide the user to timing hotspots for timing related model revisions
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