Architecture of the Component Collective Messaging Interface.
ABSTRACT Different programming paradigms utilize a variety of collective communication operations, often with different semantics. We present the component collective messaging interface (CCMI) that can support asynchronous non-blocking collectives and is extensible to different programming paradigms and architectures. CCMI is designed with components written in the C++ programming language, allowing it to be reusable and extendible. Collective algorithms are embodied in topological schedules and executors that execute them. Portability across architectures is enabled by the multisend data movement component. CCMI includes a programming language adaptor used to implement different APIs with different semantics for different paradigms. We study the effectiveness of CCMI on 16K nodes of Blue Gene/P machine and evaluate its performance for the barrier, broadcast, and allreduce collective operations and several application benchmarks. We also present the performance of the barrier collective on the Abe Infiniband cluster.
Conference Paper: Optimization of MPI_Allreduce on the blue Gene/Q supercomputer[Show abstract] [Hide abstract]
ABSTRACT: The IBM Blue Gene/Q supercomputer has a 5D torus network where each node is connected to ten bi-directional links. In this paper we present techniques to optimize the MPI_Allreduce collective operation by building ten different edge disjoint spanning trees on the ten torus links. We accelerate summing of network packets with local buffers by the use of Quad Processing SIMD unit in the BG/Q cores and executing the sums on multiple communication threads created by the PAMI libraries. The net gain we achieve is a peak throughput of 6.3 GB/sec for double precision floating point sum allreduce, that is a speedup of 3.75x over the collective network based algorithm in the product MPI stack on BG/Q.Proceedings of the 20th European MPI Users' Group Meeting; 09/2013
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ABSTRACT: The Blue Gene/P (BG/P) supercomputer consists of thousands of compute nodes interconnected by multiple networks. Out of these, a 3D torus equipped with direct memory access (DMA) engine is the primary network. BG/P also features a collective network which supports hardware accelerated collective operations such as broadcast and all reduce. One of the operating modes on BG/P is the virtual node mode where the four cores can be active MPI tasks, performing inter-node and intra-node communication. This paper proposes software techniques to enhance MPI Collective communication primitives, MPI Bcast and MPI Allreduce in virtual node mode by using cache coherent memory subsystem as the communication method within the node. The paper describes techniques leveraging atomic operations to design concurrent data structures such as broadcast-FIFOs to enable efficient collectives. Such mechanisms are important as we expect the core counts to rise in the future and having such data structures makes programming easier and efficient. We also demonstrate the utility of shared address space techniques for MPI collectives, wherein a process can access the peer's memory by specialized system calls. Apart from cutting down the copy costs, such techniques allow for seamless integration of network protocols with intra-node communication methods. We propose intra-node extensions to multi-color network algorithms for collectives using light weight synchronizing structures and atomic operations. Further, we demonstrate that shared address techniques allow for good load balancing and are critical for efficiently using the hardware collective network on BG/P. When compared to current approaches on the 3D torus, our optimizations provide performance up to almost 3 folds for MPI Bcast and a 33% performance gain for MPI Allreduce(in virtual node mode). We also see improvements up to 44% for MPI Bcast using the collective tree network.Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on; 06/2011
Conference Paper: MPI on a Million Processors.[Show abstract] [Hide abstract]
ABSTRACT: Petascale machines with close to a million processors will soon be available. Although MPI is the dominant programming model today, some researchers and users wonder (and perhaps even doubt) whether MPI will scale to such large processor counts. In this paper, we examine this issue of how scalable is MPI. We first examine the MPI specification itself and discuss areas with scalability concerns and how they can be overcome. We then investigate issues that an MPI implemen- tation must address to be scalable. We ran some experiments to measure MPI memory consumption at scale on up to 131,072 processes or 80% of the IBM Blue Gene/P system at Argonne National Laboratory. Based on the results, we tuned the MPI implementation to reduce its memory footprint. We also discuss issues in application algorithmic scalability to large process counts and features of MPI that enable the use of other techniques to overcome scalability limitations in applications.Recent Advances in Parallel Virtual Machine and Message Passing Interface, 16th European PVM/MPI Users' Group Meeting, Espoo, Finland, September 7-10, 2009. Proceedings; 01/2009