2,638 research outputs found
Design and Implementation of MPICH2 over InfiniBand with RDMA Support
For several years, MPI has been the de facto standard for writing parallel
applications. One of the most popular MPI implementations is MPICH. Its
successor, MPICH2, features a completely new design that provides more
performance and flexibility. To ensure portability, it has a hierarchical
structure based on which porting can be done at different levels. In this
paper, we present our experiences designing and implementing MPICH2 over
InfiniBand. Because of its high performance and open standard, InfiniBand is
gaining popularity in the area of high-performance computing. Our study focuses
on optimizing the performance of MPI-1 functions in MPICH2. One of our
objectives is to exploit Remote Direct Memory Access (RDMA) in Infiniband to
achieve high performance. We have based our design on the RDMA Channel
interface provided by MPICH2, which encapsulates architecture-dependent
communication functionalities into a very small set of functions. Starting with
a basic design, we apply different optimizations and also propose a
zero-copy-based design. We characterize the impact of our optimizations and
designs using microbenchmarks. We have also performed an application-level
evaluation using the NAS Parallel Benchmarks. Our optimized MPICH2
implementation achieves 7.6 s latency and 857 MB/s bandwidth, which are
close to the raw performance of the underlying InfiniBand layer. Our study
shows that the RDMA Channel interface in MPICH2 provides a simple, yet
powerful, abstraction that enables implementations with high performance by
exploiting RDMA operations in InfiniBand. To the best of our knowledge, this is
the first high-performance design and implementation of MPICH2 on InfiniBand
using RDMA support.Comment: 12 pages, 17 figure
ScALPEL: A Scalable Adaptive Lightweight Performance Evaluation Library for application performance monitoring
As supercomputers continue to grow in scale and capabilities, it is becoming
increasingly difficult to isolate processor and system level causes of
performance degradation. Over the last several years, a significant number of
performance analysis and monitoring tools have been built/proposed. However,
these tools suffer from several important shortcomings, particularly in
distributed environments. In this paper we present ScALPEL, a Scalable Adaptive
Lightweight Performance Evaluation Library for application performance
monitoring at the functional level. Our approach provides several distinct
advantages. First, ScALPEL is portable across a wide variety of architectures,
and its ability to selectively monitor functions presents low run-time
overhead, enabling its use for large-scale production applications. Second, it
is run-time configurable, enabling both dynamic selection of functions to
profile as well as events of interest on a per function basis. Third, our
approach is transparent in that it requires no source code modifications.
Finally, ScALPEL is implemented as a pluggable unit by reusing existing
performance monitoring frameworks such as Perfmon and PAPI and extending them
to support both sequential and MPI applications.Comment: 10 pages, 4 figures, 2 table
On the acceleration of wavefront applications using distributed many-core architectures
In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P). Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures
An investigation of the performance portability of OpenCL
This paper reports on the development of an MPI/OpenCL implementation of LU, an application-level benchmark from the NAS Parallel Benchmark Suite. An account of the design decisions addressed during the development of this code is presented, demonstrating the importance of memory arrangement and work-item/work-group distribution strategies when applications are deployed on different device types. The resulting platform-agnostic, single source application is benchmarked on a number of different architectures, and is shown to be 1.3–1.5× slower than native FORTRAN 77 or CUDA implementations on a single node and 1.3–3.1× slower on multiple nodes. We also explore the potential performance gains of OpenCL’s device fissioning capability, demonstrating up to a 3× speed-up over our original OpenCL implementation
- …