C++ programming language for an abstract massively parallel SIMD architecture

The aim of this work is to define and implement an extended C++ language to support the SIMD programming paradigm. The C++ programming language has been extended to express all the potentiality of an abstract SIMD machine consisting of a central Control Processor and a N-dimensional toroidal array of Numeric Processors. Very few extensions have been added to the standard C++ with the goal of minimising the effort for the programmer in learning a new language and to keep very high the performance of the compiled code. The proposed language has been implemented as a porting of the GNU C++ Compiler on a SIMD supercomputer.Comment: 10 page

High performance FORTRAN without templates: An alternative model for distribution and alignment

Language extensions of FORTRAN are being developed which permit the user to map data structures to the individual processors of distributed memory machines. These languages allow a programming style in which global data references are used. Current efforts are focussed on designing a common basis for such languages, the result of which is known as High Performance Fortran (HPF). One of the central debates in the HPF effort revolves around the concept of templates, introduced as an abstract index space to which data could be aligned. A model for the mapping of data which provides the functionality of High Performance Fortran distributions without the use of templates is presented

Method for resource control in parallel environments using program organization and run-time support

A system and method for dynamic scheduling and allocation of resources to parallel applications during the course of their execution. By establishing well-defined interactions between an executing job and the parallel system, the system and method support dynamic reconfiguration of processor partitions, dynamic distribution and redistribution of data, communication among cooperating applications, and various other monitoring actions. The interactions occur only at specific points in the execution of the program where the aforementioned operations can be performed efficiently

Method for resource control in parallel environments using program organization and run-time support

A system and method for dynamic scheduling and allocation of resources to parallel applications during the course of their execution. By establishing well-defined interactions between an executing job and the parallel system, the system and method support dynamic reconfiguration of processor partitions, dynamic distribution and redistribution of data, communication among cooperating applications, and various other monitoring actions. The interactions occur only at specific points in the execution of the program where the aforementioned operations can be performed efficiently

Supporting Irregular Distributions in FORTRAN 90D/HPF Compilers

This paper presents methods that make it possible to efficiently support irregular problems using data parallel languages. The approach involves the use of a portable, compiler-independent, runtime support library called CHAOS. The CHAOS runtime support library contains procedures that (1) support static and dynamic distributed array partitioning, (2) partition loop iterations and indirection arrays, (3) remap arrays from one distribution to another, and (4) carry out index translation, buffer allocation and communication schedule generation. The CHAOS runtime procedures are used by a prototype Fortran 90D compiler as runtime support for irregular problems. This paper also presents performance results of compiler-generated and hand-parallelized versions of two stripped down applications codes. The first code is derived from an unstructured mesh computational fluid dynamics flow solver and the second is derived from the molecular dynamics code CHARMM. A method is described that makes it possible to emulate irregular distributions in HPF by reordering elements of data arrays and renumbering indirection arrays. The results suggest that HPF compiler could use reordering and renumbering extrinsic functions to obtain performance comparable to that achieved by a compiler for a language (such as Fortran 90D) that directly supports irregular distributions

Macroservers: An Execution Model for DRAM Processor-In-Memory Arrays

The emergence of semiconductor fabrication technology allowing a tight coupling between high-density DRAM and CMOS logic on the same chip has led to the important new class of Processor-In-Memory (PIM) architectures. Newer developments provide powerful parallel processing capabilities on the chip, exploiting the facility to load wide words in single memory accesses and supporting complex address manipulations in the memory. Furthermore, large arrays of PIMs can be arranged into a massively parallel architecture. In this report, we describe an object-based programming model based on the notion of a macroserver. Macroservers encapsulate a set of variables and methods; threads, spawned by the activation of methods, operate asynchronously on the variables' state space. Data distributions provide a mechanism for mapping large data structures across the memory region of a macroserver, while work distributions allow explicit control of bindings between threads and data. Both data and work distributuions are first-class objects of the model, supporting the dynamic management of data and threads in memory. This offers the flexibility required for fully exploiting the processing power and memory bandwidth of a PIM array, in particular for irregular and adaptive applications. Thread synchronization is based on atomic methods, condition variables, and futures. A special type of lightweight macroserver allows the formulation of flexible scheduling strategies for the access to resources, using a monitor-like mechanism

Directions in parallel programming: HPF, shared virtual memory and object parallelism in pC++

Fortran and C++ are the dominant programming languages used in scientific computation. Consequently, extensions to these languages are the most popular for programming massively parallel computers. We discuss two such approaches to parallel Fortran and one approach to C++. The High Performance Fortran Forum has designed HPF with the intent of supporting data parallelism on Fortran 90 applications. HPF works by asking the user to help the compiler distribute and align the data structures with the distributed memory modules in the system. Fortran-S takes a different approach in which the data distribution is managed by the operating system and the user provides annotations to indicate parallel control regions. In the case of C++, we look at pC++ which is based on a concurrent aggregate parallel model

Compilation techniques for irregular problems on parallel machines

Massively parallel computers have ushered in the era of teraflop computing. Even though large and powerful machines are being built, they are used by only a fraction of the computing community. The fundamental reason for this situation is that parallel machines are difficult to program. Development of compilers that automatically parallelize programs will greatly increase the use of these machines.;A large class of scientific problems can be categorized as irregular computations. In this class of computation, the data access patterns are known only at runtime, creating significant difficulties for a parallelizing compiler to generate efficient parallel codes. Some compilers with very limited abilities to parallelize simple irregular computations exist, but the methods used by these compilers fail for any non-trivial applications code.;This research presents development of compiler transformation techniques that can be used to effectively parallelize an important class of irregular programs. A central aim of these transformation techniques is to generate codes that aggressively prefetch data. Program slicing methods are used as a part of the code generation process. In this approach, a program written in a data-parallel language, such as HPF, is transformed so that it can be executed on a distributed memory machine. An efficient compiler runtime support system has been developed that performs data movement and software caching

PASSION: Parallel And Scalable Software for Input-Output

We are developing a software system called PASSION: Parallel And Scalable Software for Input-Output which provides software support for high performance parallel I/O. PASSION provides support at the language, compiler, runtime as well as file system level. PASSION provides runtime procedures for parallel access to files (read/write), as well as for out-of-core computations. These routines can either be used together with a compiler to translate out-of-core data parallel programs written in a language like HPF, or used directly by application programmers. A number of optimizations such as Two-Phase Access, Data Sieving, Data Prefetching and Data Reuse have been incorporated in the PASSION Runtime Library for improved performance. PASSION also provides an initial framework for runtime support for out-of-core irregular problems. The goal of the PASSION compiler is to automatically translate out- of-core data parallel programs to node programs for distributed memory machines, with calls to the PASSION Runtime Library. At the language level, PASSION suggests extensions to HPF for out-of-core programs. At the file system level, PASSION provides support for buffering and prefetching data from disks. A portable parallel file system is also being developed as part of this project, which can be used across homogeneous or heterogeneous networks of workstations. PASSION also provides support for integrating data and task parallelism using parallel I/O techniques. We have used PASSION to implement a number of out-of-core applications such as a Laplace\u27s equation solver, 2D FFT, Matrix Multiplication, LU Decomposition, image processing applications as well as unstructured mesh kernels in molecular dynamics and computational fluid dynamics. We are currently in the process of using PASSION in applications in CFD (3D turbulent flows), molecular structure calculations, seismic computations, and earth and space science applications such as Four-Dimensional Data Assimilation. PASSION is currently available on the Intel Paragon, Touchstone Delta and iPSC/860. Efforts are underway to port it to the IBM SP-1 and SP-2 using the Vesta Parallel File System