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217
A survey of generalpurpose computation on graphics hardware
, 2007
"... The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware acompelling platform for computationally demanding tasks in awide variety of application domains. In this report, we describe, summarize, and analyze the l ..."
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Cited by 319 (16 self)
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The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware acompelling platform for computationally demanding tasks in awide variety of application domains. In this report, we describe, summarize, and analyze the latest research in mapping generalpurpose computation to graphics hardware. We begin with the technical motivations that underlie generalpurpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim the main body of this report at two separate audiences. First, we describe the techniques used in mapping generalpurpose computation to graphics hardware. We believe these techniques will be generally useful for researchers who plan to develop the next generation of GPGPU algorithms and techniques. Second, we survey and categorize the latest developments in generalpurpose application development on graphics hardware.
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
 ACM Trans. Graph
, 2003
"... Permission to make digital/hard copy of part of all of this work for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication, and its date appear, and notice is given ..."
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Cited by 216 (3 self)
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Permission to make digital/hard copy of part of all of this work for personal or classroom use is granted without fee provided that the copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication, and its date appear, and notice is given that copying is by permission
Programming Parallel Algorithms
, 1996
"... In the past 20 years there has been treftlendous progress in developing and analyzing parallel algorithftls. Researchers have developed efficient parallel algorithms to solve most problems for which efficient sequential solutions are known. Although some ofthese algorithms are efficient only in a th ..."
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Cited by 193 (9 self)
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In the past 20 years there has been treftlendous progress in developing and analyzing parallel algorithftls. Researchers have developed efficient parallel algorithms to solve most problems for which efficient sequential solutions are known. Although some ofthese algorithms are efficient only in a theoretical framework, many are quite efficient in practice or have key ideas that have been used in efficient implementations. This research on parallel algorithms has not only improved our general understanding ofparallelism but in several cases has led to improvements in sequential algorithms. Unf:ortunately there has been less success in developing good languages f:or prograftlftling parallel algorithftls, particularly languages that are well suited for teaching and prototyping algorithms. There has been a large gap between languages
Implementation of a Portable Nested DataParallel Language
 Journal of Parallel and Distributed Computing
, 1994
"... This paper gives an overview of the implementation of Nesl, a portable nested dataparallel language. This language and its implementation are the first to fully support nested data structures as well as nested dataparallel function calls. These features allow the concise description of parallel alg ..."
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Cited by 177 (26 self)
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This paper gives an overview of the implementation of Nesl, a portable nested dataparallel language. This language and its implementation are the first to fully support nested data structures as well as nested dataparallel function calls. These features allow the concise description of parallel algorithms on irregular data, such as sparse matrices and graphs. In addition, they maintain the advantages of dataparallel languages: a simple programming model and portability. The current Nesl implementation is based on an intermediate language called Vcode and a library of vector routines called Cvl. It runs on the Connection Machine CM2, the Cray YMP C90, and serial machines. We compare initial benchmark results of Nesl with those of machinespecific code on these machines for three algorithms: leastsquares linefitting, median finding, and a sparsematrix vector product. These results show that Nesl's performance is competitive with that of machinespecific codes for regular dense da...
A Comparison of Sorting Algorithms for the Connection Machine CM2
"... We have implemented three parallel sorting algorithms on the Connection Machine Supercomputer model CM2: Batcher's bitonic sort, a parallel radix sort, and a sample sort similar to Reif and Valiant's flashsort. We have also evaluated the implementation of many other sorting algorithms proposed in t ..."
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Cited by 173 (6 self)
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We have implemented three parallel sorting algorithms on the Connection Machine Supercomputer model CM2: Batcher's bitonic sort, a parallel radix sort, and a sample sort similar to Reif and Valiant's flashsort. We have also evaluated the implementation of many other sorting algorithms proposed in the literature. Our computational experiments show that the sample sort algorithm, which is a theoretically efficient "randomized" algorithm, is the fastest of the three algorithms on large data sets. On a 64Kprocessor CM2, our sample sort implementation can sort 32 10 6 64bit keys in 5.1 seconds, which is over 10 times faster than the CM2 library sort. Our implementation of radix sort, although not as fast on large data sets, is deterministic, much simpler to code, stable, faster with small keys, and faster on small data sets (few elements per processor). Our implementation of bitonic sort, which is pipelined to use all the hypercube wires simultaneously, is the least efficient of the three on large data sets, but is the most efficient on small data sets, and is considerably more space efficient. This paper analyzes the three algorithms in detail and discusses many practical issues that led us to the particular implementations.
Models and Languages for Parallel Computation
 ACM COMPUTING SURVEYS
, 1998
"... We survey parallel programming models and languages using 6 criteria [:] should be easy to program, have a software development methodology, be architectureindependent, be easy to understand, guranatee performance, and provide info about the cost of programs. ... We consider programming models in ..."
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Cited by 134 (4 self)
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We survey parallel programming models and languages using 6 criteria [:] should be easy to program, have a software development methodology, be architectureindependent, be easy to understand, guranatee performance, and provide info about the cost of programs. ... We consider programming models in 6 categories, depending on the level of abstraction they provide.
Nesl: A Nested DataParallel Language
, 1990
"... This report describes Nesl, a stronglytyped, applicative, dataparallel language. Nesl is intended to be used as a portable interface for programming a variety of parallel and vector supercomputers, and as a basis for teaching parallel algorithms. Parallelism is supplied through a simple set of dat ..."
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Cited by 134 (4 self)
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This report describes Nesl, a stronglytyped, applicative, dataparallel language. Nesl is intended to be used as a portable interface for programming a variety of parallel and vector supercomputers, and as a basis for teaching parallel algorithms. Parallelism is supplied through a simple set of dataparallel constructs based on vectors, including a mechanism for applying any function over the elements of a vector in parallel, and a broad set of parallel functions that manipulate vectors. Nesl fully supports nested vectors and nested parallelismthe ability to take a parallel function and then apply it over multiple instances in parallel. Nested parallelism is important for implementing algorithms with complex and dynamically changing data structures, such as required in many graph or sparse matrix algorithms. Nesl also provides a mechanism for calculating the asymptotic running time for a program on various parallel machine models, including the parallel random access machine (PRAM...
Scan Primitives for GPU Computing
 GRAPHICS HARDWARE 2007
, 2007
"... The scan primitives are powerful, generalpurpose dataparallel primitives that are building blocks for a broad range of applications. We describe GPU implementations of these primitives, specifically an efficient formulation and implementation of segmented scan, on NVIDIA GPUs using the CUDA API.Us ..."
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Cited by 111 (8 self)
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The scan primitives are powerful, generalpurpose dataparallel primitives that are building blocks for a broad range of applications. We describe GPU implementations of these primitives, specifically an efficient formulation and implementation of segmented scan, on NVIDIA GPUs using the CUDA API.Using the scan primitives, we show novel GPU implementations of quicksort and sparse matrixvector multiply, and analyze the performance of the scan primitives, several sort algorithms that use the scan primitives, and a graphical shallowwater fluid simulation using the scan framework for a tridiagonal matrix solver.
Geometric Mesh Partitioning: Implementation and Experiments
"... We investigate a method of dividing an irregular mesh into equalsized pieces with few interconnecting edges. The method’s novel feature is that it exploits the geometric coordinates of the mesh vertices. It is based on theoretical work of Miller, Teng, Thurston, and Vavasis, who showed that certain ..."
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Cited by 102 (19 self)
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We investigate a method of dividing an irregular mesh into equalsized pieces with few interconnecting edges. The method’s novel feature is that it exploits the geometric coordinates of the mesh vertices. It is based on theoretical work of Miller, Teng, Thurston, and Vavasis, who showed that certain classes of “wellshaped” finite element meshes have good separators. The geometric method is quite simple to implement: we describe a Matlab code for it in some detail. The method is also quite efficient and effective: we compare it with some other methods, including spectral bisection.
NESL: A nested dataparallel language (version 2.6
, 1993
"... The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Wright Laboratory or the U. S. Government. Keywords: Dataparallel, parallel algorithms, supe ..."
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Cited by 95 (7 self)
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The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Wright Laboratory or the U. S. Government. Keywords: Dataparallel, parallel algorithms, supercomputers, nested parallelism, This report describes Nesl, a stronglytyped, applicative, dataparallel language. Nesl is intended to be used as a portable interface for programming a variety of parallel and vector computers, and as a basis for teaching parallel algorithms. Parallelism is supplied through a simple set of dataparallel constructs based on sequences, including a mechanism for applying any function over the elements of a sequence in parallel and a rich set of parallel functions that manipulate sequences. Nesl fully supports nested sequences and nested parallelism—the ability to take a parallel function and apply it over multiple instances in parallel. Nested parallelism is important for implementing algorithms with irregular nested loops (where the inner loop lengths depend on the outer iteration) and for divideandconquer algorithms. Nesl also provides a performance model for calculating the asymptotic performance of a program on