4,382 research outputs found
Distributed-Memory Breadth-First Search on Massive Graphs
This chapter studies the problem of traversing large graphs using the
breadth-first search order on distributed-memory supercomputers. We consider
both the traditional level-synchronous top-down algorithm as well as the
recently discovered direction optimizing algorithm. We analyze the performance
and scalability trade-offs in using different local data structures such as CSR
and DCSC, enabling in-node multithreading, and graph decompositions such as 1D
and 2D decomposition.Comment: arXiv admin note: text overlap with arXiv:1104.451
The "MIND" Scalable PIM Architecture
MIND (Memory, Intelligence, and Network Device) is an advanced parallel computer architecture for high performance computing and scalable embedded processing. It is a
Processor-in-Memory (PIM) architecture integrating both DRAM bit cells and CMOS logic devices on the same silicon die. MIND is multicore with multiple memory/processor nodes on
each chip and supports global shared memory across systems of MIND components. MIND is distinguished from other PIM architectures in that it incorporates mechanisms for efficient support of a global parallel execution model based on the semantics of message-driven multithreaded split-transaction processing. MIND is designed to operate either in conjunction with other conventional microprocessors or in standalone arrays of like devices. It also incorporates mechanisms for fault tolerance, real time execution, and active power management. This paper describes the major elements and operational methods of the MIND
architecture
Thread partitioning and value prediction for exploiting speculative thread-level parallelism
Speculative thread-level parallelism has been recently proposed as a source of parallelism to improve the performance in applications where parallel threads are hard to find. However, the efficiency of this execution model strongly depends on the performance of the control and data speculation techniques. Several hardware-based schemes for partitioning the program into speculative threads are analyzed and evaluated. In general, we find that spawning threads associated to loop iterations is the most effective technique. We also show that value prediction is critical for the performance of all of the spawning policies. Thus, a new value predictor, the increment predictor, is proposed. This predictor is specially oriented for this kind of architecture and clearly outperforms the adapted versions of conventional value predictors such as the last value, the stride, and the context-based, especially for small-sized history tables.Peer ReviewedPostprint (published version
QuEST and High Performance Simulation of Quantum Computers
We introduce QuEST, the Quantum Exact Simulation Toolkit, and compare it to
ProjectQ, qHipster and a recent distributed implementation of Quantum++. QuEST
is the first open source, OpenMP and MPI hybridised, GPU accelerated simulator
of universal quantum circuits. Embodied as a C library, it is designed so that
a user's code can be deployed seamlessly to any platform from a laptop to a
supercomputer. QuEST is capable of simulating generic quantum circuits of
general single-qubit gates and multi-qubit controlled gates, on pure and mixed
states, represented as state-vectors and density matrices, and under the
presence of decoherence. Using the ARCUS Phase-B and ARCHER supercomputers, we
benchmark QuEST's simulation of random circuits of up to 38 qubits, distributed
over up to 2048 compute nodes, each with up to 24 cores. We directly compare
QuEST's performance to ProjectQ's on single machines, and discuss the
differences in distribution strategies of QuEST, qHipster and Quantum++. QuEST
shows excellent scaling, both strong and weak, on multicore and distributed
architectures.Comment: 8 pages, 8 figures; fixed typos; updated QuEST URL and fixed typo in
Fig. 4 caption where ProjectQ and QuEST were swapped in speedup subplot
explanation; added explanation of simulation algorithm, updated bibliography;
stressed technical novelty of QuEST; mentioned new density matrix suppor
An efficient multi-core implementation of a novel HSS-structured multifrontal solver using randomized sampling
We present a sparse linear system solver that is based on a multifrontal
variant of Gaussian elimination, and exploits low-rank approximation of the
resulting dense frontal matrices. We use hierarchically semiseparable (HSS)
matrices, which have low-rank off-diagonal blocks, to approximate the frontal
matrices. For HSS matrix construction, a randomized sampling algorithm is used
together with interpolative decompositions. The combination of the randomized
compression with a fast ULV HSS factorization leads to a solver with lower
computational complexity than the standard multifrontal method for many
applications, resulting in speedups up to 7 fold for problems in our test
suite. The implementation targets many-core systems by using task parallelism
with dynamic runtime scheduling. Numerical experiments show performance
improvements over state-of-the-art sparse direct solvers. The implementation
achieves high performance and good scalability on a range of modern shared
memory parallel systems, including the Intel Xeon Phi (MIC). The code is part
of a software package called STRUMPACK -- STRUctured Matrices PACKage, which
also has a distributed memory component for dense rank-structured matrices
The Reverse Cuthill-McKee Algorithm in Distributed-Memory
Ordering vertices of a graph is key to minimize fill-in and data structure
size in sparse direct solvers, maximize locality in iterative solvers, and
improve performance in graph algorithms. Except for naturally parallelizable
ordering methods such as nested dissection, many important ordering methods
have not been efficiently mapped to distributed-memory architectures. In this
paper, we present the first-ever distributed-memory implementation of the
reverse Cuthill-McKee (RCM) algorithm for reducing the profile of a sparse
matrix. Our parallelization uses a two-dimensional sparse matrix decomposition.
We achieve high performance by decomposing the problem into a small number of
primitives and utilizing optimized implementations of these primitives. Our
implementation shows strong scaling up to 1024 cores for smaller matrices and
up to 4096 cores for larger matrices
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