VFC: The Vienna Fortran Compiler

Abstract

High Performance Fortran (HPF) offers an attractive high‐level language interface for programming scalable parallel architectures providing the user with directives for the specification of data distribution and delegating to the compiler the task of generating an explicitly parallel program. Available HPF compilers can handle regular codes quite efficiently, but dramatic performance losses may be encountered for applications which are based on highly irregular, dynamically changing data structures and access patterns. In this paper we introduce the Vienna Fortran Compiler (VFC), a new source‐to‐source parallelization system for HPF+, an optimized version of HPF, which addresses the requirements of irregular applications. In addition to extended data distribution and work distribution mechanisms, HPF+ provides the user with language features for specifying certain information that decisively influence a program’s performance. This comprises data locality assertions, non‐local access specifications and the possibility of reusing runtime‐generated communication schedules of irregular loops. Performance measurements of kernels from advanced applications demonstrate that with a high‐level data parallel language such as HPF+ a performance close to hand‐written message‐passing programs can be achieved even for highly irregular codes.

Semantic-Aware Automatic Parallelization of Modern Applications Using High-Level Abstractions

“…We only mention a few of parallelizing compilers and tools for brevity. The Vienna Fortran compiler (VFC) [4] is a source-to-source parallelization system for an optimized version of High Performance Fortran. The Polaris compiler [6] is mainly used for improving loop-level automatic parallelization.…”

Gossamer: A Lightweight Approach to Using Multicore Machines

“…This approach is general and efficient, but it is not simple because the programmer has to control every aspect of concurrency and synchronization using low-level APIs. At the other extreme, one could use a compiler that automatically parallelizes code or uses optimistic concurrency techniques [8,12,10]. This is simple and it works well for some kinds of parallelism (e.g., independent loop iterations), but it is not general.…”

“…This frees the programmer from having to learn how to write a parallel program. Such compilers include Intel C compiler [10], Paradigm [29], Polaris [11] Rice Fortran D [1], SUIF [59], and Vienna Fortran [8];…”

Semantic-Driven Parallelization of Loops Operating on User-Defined Containers

“…Current compiler technology [7][8][9][10][11] can efficiently automate the introduction of OpenMP directives to regular loops that iterate over random-access arrays as defined by Fortran or C. However, because most C++ programs, including many scientific applications, use higher-level abstractions for which semantics are unknown to the compiler, these abstractions are left unoptimized by most parallelizing compilers. By providing mechanisms to optimize object-oriented library abstractions, we thus allow the efficient tailoring of the programming environment as essentially a programming language that is more domain-specific than a general purpose language could allow, thereby allowing the improvement of programmer productivity without degrading application performance.…”

“…Examples of these research compilers include the DSystem [7], the Fx compiler [8], the Vienna Fortran Compiler [9], the Paradigm compiler [10], the Polaris compiler [11], and the SUIF compiler [13]. However, except for SUIF, which has front-ends for Fortran, C, and C++; the others listed above optimize only Fortran applications.…”