2 edition of Distributed memory compiler design for sparse problems found in the catalog.
Distributed memory compiler design for sparse problems
by National Aeronautics and Space Administration, Langley Research Center in Hampton, Va
Written in English
|Statement||Janet Wu ... [et al.].|
|Series||ICASE report -- no. 91-13., NASA contractor report -- 187515., NASA contractor report -- NASA CR-187515.|
|Contributions||Wu, Janet., Langley Research Center.|
|The Physical Object|
The problem in which we will be interested concerns models of theoretical neuroscience that could explain the speed and robustness of an expert's recollection. The approach is based on Sparse Distributed Memory, which has been shown to be plausible, both in a neuroscientific and in a psychological manner, in a number of by: 1. Distributed Memory Compiler Design for Sparse Problems Published in: IEEE Transactions on Computers vol. 44, no.6, J. Wu, Raja Das, Joel Saltz, H. Berryman, S. Hiranandani This paper addresses the issue of compiling concurrent loop nests in the presence of complicated array references and irregularly distributed arrays.
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Parallel Computing 30 () – Fortran 90 provides a rich set of array intrinsic functions that are useful for representing array expressions and data parallel programming. However, the application of these intrinsic functions to sparse data sets in distributed memory environments, is currently not supported. A new method is presented for distributing data in sparse matrix-vector multiplication. The method is two-dimensional, tries to minimize the true communication volume, and also tries to spread the computation and communication work evenly over the processors. The method starts with a recursive bipartitioning of the sparse matrix, each time splitting a rectangular matrix into two parts with a Cited by:
in a simulated sparse distributed memory by addressing the memory with the pattern itself. Each pattern is a 16x16 array of bits that transforms into a bit vector. The three figures at the bottom show the result of an iterative search in which the result of the . @ThanksGuys Using a loop, you create only small vectors at a time, so that you won't have any problem of memory, as you wanted. The main difference between our implementations is that I fill the matrix column-wise, which should be more efficient, especially for sparse .
One of the virtues of the layered approach to distributed compiler design is the capture of a set of critical optimizations in the runtime support primitives. These primitives, and hence these optimizations, can be migrated to a variety of compilers targeting distributed memory multiprocessors.
Distributed Memory Compiler Design for Sparse Problems Article (PDF Available) in IEEE Transactions on Computers 44(6) - July with Reads How we measure 'reads'. Motivated by the remarkable fluidity of memory the way in which items are pulled spontaneously and effortlessly from our memory by vague similarities to what is currently occupying our attention Sparse Distributed Memory presents a mathematically elegant theory of human long term memory.
The book, which is self contained, begins with background material from mathematics, computers, and neurophysiology; this is followed by a step by step development of the memory by: COVID Resources.
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A compiler and runtime support mechanism is described and demonstrated. The methods presented are capable of solving a wide range of sparse and unstructured problems in scientific computing. The compiler takes as input a FORTRAN 77 program enhanced with specifications for distributing data, and the compiler outputs a message passing program that runs on a distributed memory computer.
Motivated by the remarkable fluidity of memory the way in which items are pulled spontaneously and effortlessly from our memory by vague similarities to what is currently occupying our attention Sparse Distributed Memory presents a mathematically elegant theory of human long term book, which is self contained, begins with background material from mathematics, computers, and neurophysiology; this is followed by a step by step development of the memory model.
tributed memory code for a large class of sparse and unstructured problems. In sparse and unstructured problems, the dependency structure is determined by variable values known only at runtime. In these cases, effective use of distributed memory architectures is made possible by a runtime preprocessing phase.
This preprocessing is used to par. Distributed Memory Compiler Methods for Irregular Problems Figure 9: Surface View of Unstructured Mesh Employed for Computing Flow over 0 N ERA M6 Wing, Number of nodes = K R.
Das et al There are a variety of compiler projects targeted at distributed memory multiprocessors , , , , , , , , , , , [ 7, 2 7, 2 6, 2 8 ] , the Kali project , Cited by: Sparse distributed memory is a generalized random-access memory (RAM) for long (e.g., 1, bit) binary words.
Such words can be written into and read from the memory, and they can also be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original write address but also by giving.
This paper provides a comprehensive study and comparison of two state-of-the-art direct solvers for large sparse sets of linear equations on large-scale distributed-memory computers.
One is a multifrontal solver called MUMPS, the other is a supernodal solver called : R AmestoyPatrick, S DuffIain, L'excellentJean-Yves, S LiXiaoye.
Janet Wu, Raja Das, Joel Saltz, Harry Berryman, and Seema Hiranandani. Distributed memory compiler design for sparse problems. IEEE Transactions on Computers, 44(6)–, June zbMATH CrossRef Google ScholarCited by: 1. () Distributed memory compiler design for sparse problems. IEEE Transactions on Computers() Parallel implementation of finite-element/newton method for solution of steady-state and transient nonlinear partial differential by: Das R, Wu J, Saltz J, Berryman H and Hiranandani S () Distributed Memory Compiler Design For Sparse Problems, IEEE Transactions on Computers,(), Online publication date: 1-Jun Benkner S Handling block-cyclic distributed arrays in Vienna Fortran 90 Proceedings of the IFIP WG working conference on Parallel.
Sparse Distributed Memory A study of psychologically driven storage Pentti Kanerva Neurons as address decoders Best match Sparse Memory Distributed Storage Sparse Distributed Memory Œ p.
2/ Goal The goal of Kanerva’s paper is to present a method of storage that, given a test vector, can retrieve the best Problem: Given a data set.
Purchase Languages, Compilers and Run-time Environments for Distributed Memory Machines, Volume 3 - 1st Edition. Print Book & E-Book. ISBNBook Edition: 1. Motivated by the remarkable fluidity of memory the way in which items are pulled spontaneously and effortlessly from our memory by vague similarities to what is currently occupying our attention "Sparse Distributed Memory "presents a mathematically elegant theory of human long term book, which is self contained, begins with background material from mathematics,/5.
A Shared- and distributed-memory parallel general sparse direct solver Article in Applicable Algebra in Engineering Communication and Computing 18(3) May with 19 Reads.
Applying a sparse privatization and a multi-loops analysis at compile-time we enhance the performance and reduce the number of extra code annotations. The building/updating of a sparse matrix at run-time is also studied in this paper, solving the problem of using pointers and some levels of indirections on the left hand : Gerardo Bandera, Emilio L.
Zapata. Sparse distributed memory. Jump to navigation Jump to search. Sparse distributed memory (SDM) is a mathematical model of human long-term memory introduced by Pentti Kanerva in while he was at NASA Ames Research Center. It is a generalized random-access memory (RAM). The proposed distributed annotation-based C# (DisBlue+) Built on the top of Blue+, DisBlue+ inherits all its features (Section 4).
Moreover, DisBlue+ has own distributed features that totally depend on compiled interface concept. The DisBlue+ idea is based on OOP features coupled with Cited by: 1. The book covers a variety of problem domains within the models, including: leader election, mutual exclusion, consensus and clock synchronization.
It presents several recent developments, including fast mutual exclusion algorithms, distributed shared memory, the wait-free hierarchy, and sparse .kernel is an irregular problem, which has led to the development of several compressed storage formats. We design a data structure for distributed matrix to compute the matrix-vector product efficiently on distributed memory parallel computers using MPI.
We conduct numerical experiments on several different sparse matrices and show the parallel. The widespread use of object-oriented languages and Internet security concerns are just the beginning. Add embedded systems, multiple memory banks, highly pipelined units operating in parallel, and a host of other advances and it becomes clear that current and future computer architectures pose immense challenges to compiler designers-challenges th4/5(2).