D. Hillis and Jr. G. Steele. Data Parallel Algorithms. Communications of the ACM, Vol. 29, pages 1170--1183, December 1986. Home/Search Document Not in Database Summary ACM TOC Related Articles Check |
This paper is cited in the following contexts:
First 50 documents Next 50
On the Implementation of an Inference Algorithm in Java - Rus (1999) (Correct)
.... systems that support a true message passing implementation tend to provide natural support for parallelism and distribution[11] 23] Those systems that implement messages as either statically or dynamically bound procedure calls do not provide such support for parallelism and distribution [22](although their conceptual model implies that they do) Another important concept underlying object orientation is the idea of a class. Classes in object oriented systems provide a means of classifying objects [10] Di erent objects that belong to a common class share identical behavior. The ....
Hillis, W., and Steele, G. Data parallel algorithms. Communications of the ACM 29, 12 (December 1986), 1170-1183.
c flCopyright by Manish Gupta, 1992 - Automatic Data Partitioning (Correct)
....the target parallel program for a multicomputer. For most compilers, this parallel program corresponds to the SPMD (Single Program Multiple Data) model [39] where all processors execute the same program, but operate on distinct data items, thus enabling the exploitation of data parallelism [28]. These research efforts include the Fortran D compiler [30, 31] and the Superb compiler [81] both accepting Fortran 77 as the base language. The Crystal compiler [15] and the Id Nouveau compiler [62] are targeted for single assignment languages. Numerous other compilers, Dataparallel C [59] ....
W. Hillis and G. Steele Jr. Data parallel algorithms. Communications of the ACM, 29(12):1170--1183, 1986.
Mixed Programming Metaphors in a Shared Dataspace Model of.. - Roman, Cunningham (2003) (13 citations) (Correct)
.... to organize the computation dynamically in response to the unpredictable structure of the data being processed (e.g. on a region by region basis in the labeling problem) Neither Linda nor the traditional approaches to concurrent computation, such as the UNITY paradigm and the data parallel [17] computing style used to write Connection Machine algorithms, can accomplish this. Linda s limitations are the result of a language development philosophy di#erent from that of Swarm a philosophy which favors an e#cient implementation over programming convenience and the capability to reason ....
W. D. Hillis and G. L. Steele Jr. Data parallel algorithms. Communications of the ACM, 29(12):1170--1183, December 1986.
Data Compiling for Systems of Affine Recurrence Equations - Mongenet (1994) (Correct)
....systems of affine recurrence equations. Keywords: parallelism, mapping, systems of recurrence equations, data parallelism, data compilation, communications. 1: Introduction Data parallelism is often considered as a general programming model in the context of massively parallel architectures ([7]) This model is founded on the virtualization of data and processes. A program is composed of parallel operations in which the mechanisms managing the data accesses are hidden. To be efficiently used this programming model requires powerful tools such as parallelizing compilers. Such a compiler ....
Hillis W.D., Steele G.L., Data parallel algorithms. Communications of the ACM, 29(12), pp. 1170--1183, 1986.
Representing and Executing Agent-Based Systems - Fisher (1994) (24 citations) (Correct)
.... is that, in recent years, low level mechanisms for efficient broadcast have been developed in many computer systems [4] Further, not only is broadcast one of the basic communication mechanisms on local area networks [19] but also the advent of novel parallel architectures (e.g. data parallelism [14, 15]) has meant that more powerful programming techniques based upon broadcast communication are beginning to be developed. 4 Implementing Agent Behaviour Having considered both the representation of behaviour within individual agents, and the communication mechanism between agents, we will now ....
W. D. Hillis and G. L. Steele. Data parallel algorithms. Comm. ACM, 29(12):1170--1183, December 1986.
Scalable Peer-to-Peer Indexing with Constant State - Considine, Florio (2002) (4 citations) (Correct)
....3.2.2 Group Maintenance Once the ring topology has been constructed, the next key component to maintaining the jump pointers is to estimate the value of m. Once m is estimated, we can divide the ring into groups This is essentially the well known pointer jumping technique in parallel computing [4]. a) Chord topology (b) Our topology Figure 3: Visual comparison between the finger tables of Chord and the jump pointers of our topology x = successor.predecessor if self.hash x.hash successor.hash successor = x notify(successor) a) Chord stabilization function if predecessor.hash ....
....changed size estimates which caused the jump pointers to be reorganized again. Since this is a distinct problem in its own right, we defer this question to Appendix A where we present a novel solution based on a distributed version of skip lists [13] and the classic pointer doubling approach [4]. For the rest of this discussion, we assume that the network size is known with sufficient accuracy to pick an appropriate m within O(log n) rounds of any changes at the cost of a constant number of edges per node. To speedup the process of assigning ranks, we first divide the ring We ....
HILLIS, W. D., AND GUY L. STEELE, J. Data parallel algorithms. Communications of the ACM 29, 12 (1986), 1170-- 1183.
Scalable Peer-to-Peer Indexing with Constant State - Considine, Florio (2002) (4 citations) (Correct)
....3.2.2 Group Maintenance Once the ring topology has been constructed, the next key component to maintaining the jump pointers is to estimate the value of #. Once # is estimated, we can divide the ring into groups This is essentially the well known pointer jumping technique in parallel computing [4]. a) Chord topology (b) Our topology Figure 3: Visual comparison between the finger tables of Chord and the jump pointers of our topology x = successor.predecessor if self.hash # x.hash # successor.hash successor = x notify(successor) a) Chord stabilization function if predecessor.hash # ....
....changed size estimates which caused the jump pointers to be reorganized again. Since this is a distinct problem in its own right, we defer this question to Appendix A where we present a novel solution based on a distributed version of skip lists [13] and the classic pointer doubling approach [4]. For the rest of this discussion, we assume that the network size is known with sufficient accuracy to pick an appropriate # within ##### ## rounds of any changes at the cost of a constant number of edges per node. To speedup the process of assigning ranks, we first divide the ring We ....
HILLIS, W. D., AND GUY L. STEELE, J. Data parallel algorithms. Communications of the ACM 29, 12 (1986), 1170-- 1183.
Executing Multithreaded Programs Efficiently - Blumofe (1995) (12 citations) (Correct)
....Likewise, the Cilk programming model and runtime system including Cilk NOW build on ideas found in earlier systems. In this section, we look at other theoretical results and systems that address scheduling issues for dynamic parallel computation. We shall not look at data parallel systems [8, 53] nor at systems focused on infrastructure such as distributed shared memory [4, 6, 29, 39, 59, 60, 66, 73, 87, 92, 93] 1.2. Previous results and related work 7 or message passing [43, 96, 104, 105] Substantial research has been reported in the theoretical literature concerning the scheduling of ....
W. Hillis and G. Steele. Data parallel algorithms. Communications of the ACM, 29(12):1170--1183, December 1986.
The Message-Driven Processor: A Multicomputer Processing.. - William Dally Roy (1992) (85 citations) (Correct)
....require hundreds of instructions to ereate a new process. This cost prohibits the use of fine grain programming models where processes typically last only a few tens of instructions. The MDP supports a broad range of parallel programming models (including shared memory [16] data parallel [17], dataflow [26] actors [1] and explicit message passing [5] by providing low overhead primitive mechanisms for communication, synchronization, and naming. Communication mechanisms are provided that permit a user level task on one node to send a message to any other node in a 4K node machine in ....
W. Daniel Hillis and Guy L. Steele. Data Parallel Algorithms. Communications of the ACM, 29(12):1170 ils3, 19s6.
Condition Graphs - Barklund, Hagner, Wafin (1988) (Correct)
....in CIM; i) if any of the resolvents produced is the empty clause then a solution has been found, ii) if there is no link to a goal atom then the proof has failed, otherwise (iii) the CG is suspeded, that is, no tests are ground. 6. CIM ON THE CONNECTION MACHINE The Connection Machine (CM) [10,11] is a novel parallel computer architecture, grown out of the observation that a fundamental bottleneck in computing is the communication between processors and memory. In the Connection Machine each processor is comparatively simple and has direct access to only a small amount of memory. As a ....
W. D. Hillis, G. L. Steele Jr., Data Parallel Algorithm," Communications of tte ACM 29 (December 1986): 1170 1183.
A Framework for Parallel Job Scheduling - Subramanian (1995) (Correct)
.... Nets [Rei85] ffl Alternating Turing Machines [CKS81, Ruz80] ffl Boolean circuits [CSV84, SV84] ffl Systolic Arrays [Kun82] ffl Associative Processors [Pot92, SKA92] ffl PRAM (several varieties: EREW, CREW, several kinds of CRCW) FW78, Gol82, SS79] ffl V RAM (data parallel) model [Ble90, HS86] Each model sprang from a different research community in response to completely different problems. As a result, these models are so far removed from each other that it is often difficult to translate research advances from one area to another. Fortunately, there finally seem to be some hints ....
W.D. Hillis and G.L. Steele Jr. Data parallel algorithms. Communications of the ACM, 29(12):1170--1183, December 1986.
Analysis of Parallelism in Recursive Functions on Recursive Data .. - Ahn, Han (Correct)
....functions with complex data flow. 1 Introduction Data parallelism implements parallel computation by simultaneously applying the same operation to each element of a data collection. This paradigm is considered as a parallel model which can solve many difficulties of parallel programming [2, 10, 20]. A single stream of program execution guarantees easier programming and better readability. Massive parallelism can be easily obtained by distributing large data collections. In functional languages, data parallelism is expressed with the following two components. parallel data collections ....
W.D. Hillis and G. Steele. Data Parallel Algorithms. Communications of the ACM, 29(12):1170--1183, 1989.
A Location-Independent ASOCS Model - Rudolph (1991) (2 citations) (Correct)
....Parallelism The main goals in creating parallel systems are increased computational speed and ease of programming. Much work in recent years has gone into extending the conventional Von Neuman (fetch and execute) computing paradigm to handle parallel schemes [Feng81] Hayn82] Hill85] [Hill86], Hwang87a] Hwang87b] Walt87] Machines that use the Von Neuman paradigm in parallel are classifed as conventional parallel machines. Examples of these are the Connection Machine [Hill85] the Supercomputer [Hwang87a] pipelined processors and systolic arrays. Research has shown that the ....
. Hillis, W.D., Guy L. Steele, Jr. "Data Parallel Algorithms. "Communications of the ACM, Vol. 29, #12. pp. 1170-1183. (December 1986).
Three-Dimensional Monte Carlo Device Simulation for.. - Architectures Henry.. (Correct)
No context found.
D. Hillis and Jr. G. Steele. Data Parallel Algorithms. Communications of the ACM, Vol. 29, pages 1170--1183, December 1986.
Self-Stabilizing Structured Ring Topology P2P Systems - And (2005) (Correct)
No context found.
W. D. Hillis and J. Guy L. Steele. Data parallel algorithms. Commun. ACM, 30(1):78--78, 1987.
Evaluating Parallel Algorithms: Theoretical and Practical Aspects - Natvig (1996) (Correct)
No context found.
W. Daniel Hillis and Guy L. Jr. Steele. Data parallel algorithms. Communications of the ACM, 29(12):1170--1183, December 1986.
Patterns for Parallel Application Programs - Massingill, Mattson, Sanders (1999) (4 citations) (Correct)
No context found.
W.D. Hillis and G.L Steele, Jr. "Data Parallel Algorithms" Comm. ACM, Vol 29 No 12 pp 11701183.
Class Notes : Programming Parallel Algorithms - Cs Fall Guy (1993) (1 citation) (Correct)
No context found.
W. Daniel Hillis and Guy L. Steele Jr. Data parallel algorithms. Comm. ACM, 29(12), December 1986. 139
OOPAL: Integrating Array Programming in Object-Oriented.. - Mougin, Ducasse (2003) (Correct)
No context found.
W. D. Hillis and J. Guy L. Steele. Data parallel algorithms. Communications of the ACM, 29(12):1170--1183, 1986.
Unresponsiveness-Tolerant Collective Communication - Pakin (2001) (Correct)
No context found.
W. Daniel Hillis and Guy L. Steele Jr. Data parallel algorithms. Communications of the ACM, 29(12):1170--1183, December 1986. Available from http://www.acm.org/pubs/articles/journals/cacm/1986-29-12/ p1170-hillis/p1170-hillis.pdf.
Reasoning about Synchronic Groups - Gruia-Catalin Roman And (1992) (1 citation) (Correct)
No context found.
W. D. Hillis and G. L. Steele Jr. Data parallel algorithms. Communications of the ACM, 29(12):1170--1183, December 1986.
Thinking in Parallel: Some Basic Data-Parallel Algorithms and.. - Vishkin (2002) (1 citation) (Correct)
No context found.
W.D. Hillis and G.L. Steele. Data parallel algorithms. Comm. ACM, 29(12):1170--1183, 1986.
Unknown - (Correct)
No context found.
D. Hillis and G. Steele Jr., Data parallel algorithms. Comm. of the ACM, 29:1170-1183, 1986.
Computational Structure of the N-body Problem - Katzenelson (1989) (21 citations) (Correct)
No context found.
D. Hillis and G. Steele Jr., Data parallel algorithms. Comm. of the ACM, 29:1170-1183, 1986.
Descriptive Simplicity in Parallel Computing - Marr (1997) (Correct)
No context found.
W Daniel Hillis and Jr Guy L Steele. Data parallel algorithms. Communications of the ACM, 29(12):1170--1183, December 1986.
First 50 documents Next 50
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC