58,890 research outputs found
A High-Level Compilation Toolchain for Heterogeneous Systems
ABSTRACT This paper describes Harmonic, a toolchain that targets multiprocessor heterogeneous systems comprising different types of processing elements such as generalpurposed processors (GPPs), digital signal processors (DSP), and field-programmable gate arrays (FPGAs) from a high-level C program. The main goal of Harmonic is to improve an application by partitioning and optimising each part of the program, and selecting the most appropriate processing element in the system to execute each part. The core tools include a task transformation engine, a mapping selector, a data representation optimiser, and a hardware synthesiser. We also use the C language with source-annotations as intermediate representation for the toolchain, making it easier for users to understand and to control the compilation process
A Language and Hardware Independent Approach to Quantum-Classical Computing
Heterogeneous high-performance computing (HPC) systems offer novel
architectures which accelerate specific workloads through judicious use of
specialized coprocessors. A promising architectural approach for future
scientific computations is provided by heterogeneous HPC systems integrating
quantum processing units (QPUs). To this end, we present XACC (eXtreme-scale
ACCelerator) --- a programming model and software framework that enables
quantum acceleration within standard or HPC software workflows. XACC follows a
coprocessor machine model that is independent of the underlying quantum
computing hardware, thereby enabling quantum programs to be defined and
executed on a variety of QPUs types through a unified application programming
interface. Moreover, XACC defines a polymorphic low-level intermediate
representation, and an extensible compiler frontend that enables language
independent quantum programming, thus promoting integration and
interoperability across the quantum programming landscape. In this work we
define the software architecture enabling our hardware and language independent
approach, and demonstrate its usefulness across a range of quantum computing
models through illustrative examples involving the compilation and execution of
gate and annealing-based quantum programs
On the Implementation of GNU Prolog
GNU Prolog is a general-purpose implementation of the Prolog language, which
distinguishes itself from most other systems by being, above all else, a
native-code compiler which produces standalone executables which don't rely on
any byte-code emulator or meta-interpreter. Other aspects which stand out
include the explicit organization of the Prolog system as a multipass compiler,
where intermediate representations are materialized, in Unix compiler
tradition. GNU Prolog also includes an extensible and high-performance finite
domain constraint solver, integrated with the Prolog language but implemented
using independent lower-level mechanisms. This article discusses the main
issues involved in designing and implementing GNU Prolog: requirements, system
organization, performance and portability issues as well as its position with
respect to other Prolog system implementations and the ISO standardization
initiative.Comment: 30 pages, 3 figures, To appear in Theory and Practice of Logic
Programming (TPLP); Keywords: Prolog, logic programming system, GNU, ISO,
WAM, native code compilation, Finite Domain constraint
Description and Optimization of Abstract Machines in a Dialect of Prolog
In order to achieve competitive performance, abstract machines for Prolog and
related languages end up being large and intricate, and incorporate
sophisticated optimizations, both at the design and at the implementation
levels. At the same time, efficiency considerations make it necessary to use
low-level languages in their implementation. This makes them laborious to code,
optimize, and, especially, maintain and extend. Writing the abstract machine
(and ancillary code) in a higher-level language can help tame this inherent
complexity. We show how the semantics of most basic components of an efficient
virtual machine for Prolog can be described using (a variant of) Prolog. These
descriptions are then compiled to C and assembled to build a complete bytecode
emulator. Thanks to the high level of the language used and its closeness to
Prolog, the abstract machine description can be manipulated using standard
Prolog compilation and optimization techniques with relative ease. We also show
how, by applying program transformations selectively, we obtain abstract
machine implementations whose performance can match and even exceed that of
state-of-the-art, highly-tuned, hand-crafted emulators.Comment: 56 pages, 46 figures, 5 tables, To appear in Theory and Practice of
Logic Programming (TPLP
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
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Silicon compilation
Silicon compilation is a term used for many different purposes. In this paper we define silicon compilation as a mapping from some higher level description into layout. We define the basic issues in structural and behavioral silicon compilation and some possible solutions to those issues. Finally, we define the concept of an intelligent silicon compiler in which the compiler evaluates the quality of the generated design and attempts to improve it if it is not satisfactory
PENCIL: Towards a Platform-Neutral Compute Intermediate Language for DSLs
We motivate the design and implementation of a platform-neutral compute
intermediate language (PENCIL) for productive and performance-portable
accelerator programming
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