ReSHAPE: A Framework for Dynamic Resizing and Scheduling of Homogeneous Applications in a Parallel Environment

Applications in science and engineering often require huge computational resources for solving problems within a reasonable time frame. Parallel supercomputers provide the computational infrastructure for solving such problems. A traditional application scheduler running on a parallel cluster only supports static scheduling where the number of processors allocated to an application remains fixed throughout the lifetime of execution of the job. Due to the unpredictability in job arrival times and varying resource requirements, static scheduling can result in idle system resources thereby decreasing the overall system throughput. In this paper we present a prototype framework called ReSHAPE, which supports dynamic resizing of parallel MPI applications executed on distributed memory platforms. The framework includes a scheduler that supports resizing of applications, an API to enable applications to interact with the scheduler, and a library that makes resizing viable. Applications executed using the ReSHAPE scheduler framework can expand to take advantage of additional free processors or can shrink to accommodate a high priority application, without getting suspended. In our research, we have mainly focused on structured applications that have two-dimensional data arrays distributed across a two-dimensional processor grid. The resize library includes algorithms for processor selection and processor mapping. Experimental results show that the ReSHAPE framework can improve individual job turn-around time and overall system throughput.Comment: 15 pages, 10 figures, 5 tables Submitted to International Conference on Parallel Processing (ICPP'07

Scaling non-regular shared-memory codes by reusing custom loop schedules

In this paper we explore the idea of customizing and reusing loop schedules to improve the scalability of non-regular numerical codes in shared-memory architectures with non-uniform memory access latency. The main objective is to implicitly setup affinity links between threads and data, by devising loop schedules that achieve balanced work distribution within irregular data spaces and reusing them as much as possible along the execution of the program for better memory access locality. This transformation provides a great deal of flexibility in optimizing locality, without compromising the simplicity of the shared-memory programming paradigm. In particular, the programmer does not need to explicitly distribute data between processors. The paper presents practical examples from real applications and experiments showing the efficiency of the approach.Peer ReviewedPostprint (author's final draft

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

Optimal Compilation of HPF Remappings

International audienceApplications with varying array access patterns require to dynamically change array mappings on distributed-memory parallel machines. HPF (High Performance Fortran) provides such remappings, on data that can be replicated, explicitly through therealign andredistribute directives and implicitly at procedure calls and returns. However such features are left out of the HPF subset or of the currently discussed hpf kernel for effeciency reasons. This paper presents a new compilation technique to handle hpf remappings for message-passing parallel architectures. The first phase is global and removes all useless remappings that appear naturally in procedures. The code generated by the second phase takes advantage of replications to shorten the remapping time. It is proved optimal: A minimal number of messages, containing only the required data, is sent over the network. The technique is fully implemented in HPFC, our prototype HPF compiler. Experiments were performed on a Dec Alpha farm

Programming distributed memory architectures using Kali

Programming nonshared memory systems is more difficult than programming shared memory systems, in part because of the relatively low level of current programming environments for such machines. A new programming environment is presented, Kali, which provides a global name space and allows direct access to remote data values. In order to retain efficiency, Kali provides a system on annotations, allowing the user to control those aspects of the program critical to performance, such as data distribution and load balancing. The primitives and constructs provided by the language is described, and some of the issues raised in translating a Kali program for execution on distributed memory systems are also discussed

Affine Loop Optimization Based on Modulo Unrolling in Chapel

This work presents modulo unrolling without unrolling (modulo unrolling WU), a method for message aggregation for parallel loops in message passing programs that use affine array accesses in Chapel, a Partitioned Global Address Space (PGAS) parallel programming language. Messages incur a non-trivial run time overhead, a significant component of which is independent of the size of the message. Therefore, aggregating messages improves performance. Our optimization for message aggregation is based on a technique known as modulo unrolling, pioneered by Barua [1] whose purpose was to ensure a statically predictable single tile number for each memory reference for tiled architectures, such as the MIT Raw Machine [2]. Modulo unrolling WU applies to data that is distributed in a cyclic or block-cyclic manner. In this paper, we adapt the aforementioned modulo unrolling technique to the difficult problem of efficiently compiling PGAS languages to message passing architectures. When applied to loops and data distributed cyclically or block-cyclically, modulo unrolling WU can decide when to aggregate messages thereby reducing the overall message count and runtime for a particular loop. Compared to other methods, modulo unrolling WU greatly simplifies the complex problem of automatic code generation of message passing code. It also results in substantial performance improvements in both runtime and communication compared to the non-optimized Chapel compiler. To implement this optimization in Chapel, we modify the Cyclic distribution module's follower iterator and the Block Cyclic distribution module's leader and follower iterators, as opposed to creating a traditional compiler transformation. Results were collected that compare the performance of Chapel programs optimized with modulo unrolling WU and Chapel programs using the existing Chapel data distributions. Data collected on a ten-locale cluster show that on average, modulo unrolling WU used with Chapel's Cyclic distribution results in 64 percent fewer messages and a 36 percent decrease in runtime for our suite of benchmarks. Similarly, modulo unrolling WU used with Chapel's Block Cyclic distribution results in 72 percent fewer messages and a 53 percent decrease in runtime. Finally, the results from three different scaling experiments suggest that the greatest improvements from modulo unrolling WU occur when parallel follower iterator chunks of work contain the greatest number of data elements

Run-time and compile-time support for adaptive irregular problems

In adaptive irregular problems the data arrays are accessed via indirection arrays, and data access patterns change during computation. Implementing such problems on distributed memory machines requires support for dynamic data partitioning, efficient preprocessing and fast data migration. This research presents efficient runtime primitives for such problems. This new set of primitives is part of the CHAOS library. It subsumes the previous PARTI library which targeted only static irregular problems. To demonstrate the efficacy of the runtime support, two real adaptive irregular applications have been parallelized using CHAOS primitives: a molecular dynamics code (CHARMM) and a particle-in-cell code (DSMC). The paper also proposes extensions to Fortran D which can allow compilers to generate more efficient code for adaptive problems. These language extensions have been implemented in the Syracuse Fortran 90D/HPF prototype compiler. The performance of the compiler parallelized codes is compared with the hand parallelized versions