Synchronization elimination in the deposit model

Hinrichs, Susan

doi:10.1109/icpp.1996.538563

Cited by 2 publications

(5 citation statements)

References 9 publications

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“…Assuming then that the resulting PENDING INi = fP1; P 2 ; :::; Pmg, each P l 2 PENDING INi can be computed as P l = _cP jl where _c is performed over the j values. We note that this algorithm is more accurate and faster than those proposed in [8] and [6].…”

Section: Optimizing Synchronizationmentioning

confidence: 82%

“…Instead, our approach is based on a linear algebra framework, and can represent all HPF-like alignment and distribution information accurately. The approaches given in [6] and [8] examine the problem of eliminating redundant synchronization operations by piggy-backing them on data messages. Our approach is superior to both of these in eliminating synchronization as explained in the paper.…”

Section: Preliminary Resultsmentioning

confidence: 99%

“…Then, if the following holds, the Synch message from p to q can be eliminated: 9j j 1 j k^q 2 ConsumersFort j ; p An interesting case occurs when all the Synch messages contained in the Pendingi set for a specific i are eliminated. We can formalize this condition as 8p8qp;q 2 Pendingi 9jp; q 2 SendSett j (6) We note that the condition (7) implies the algorithms offered by [6] and [8].…”

Section: Optimizing Synchronizationmentioning

confidence: 99%

“…That is, after our analysis, for every i, SynchSeti = Pendingi ,c fp; q j 9 j s. t. (8) q 2 ConsumersFort j ; p g_cSynchFixi (9) As an example, let us consider the program shown in Figure 6 respectively. Notice that, by using the condition given in (6), the synchronization requirements for 1 can be eliminated; that is, the communications occurring in 2 and 3 together kill the synchronization requirements of the communication in where f i P k = P k ,c SendSeti .…”

Section: Optimizing Synchronizationmentioning

confidence: 99%

“…In this paper, we focus on compilation of programs annotated by HPF-like directives with one-way communication operations Put and Synch, introduced by Gupta and Schonberg [6] and Hinrichs [8]. Let us consider Figure 1(a); here, a consumer processor sends a Synch message to the producer informing that the producer can put data in a buffer physically located in the consumer's memory.…”

Section: Introductionmentioning

confidence: 99%

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Minimizing data and synchronization costs in one-way communication

Kandemir

Shenoy²,

Banerjee³

et al.

Proceedings. 1998 International Conference on Parallel Processing (Cat. No.98EX205)

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In contrast to the conventional send/receive model, the one-way communication model-using Put and Synch-allows the decoupling of message transmission from synchronization. This opens up new opportunities not only to further optimize communication but also to reduce synchronization overhead. In this paper, we present a general technique which uses a global dataflow framework to optimize communication and synchronization in the context of the oneway communication model. Our approach works with the most general data alignments and distributions in languages like HPF, and is more powerful than other current solutions for eliminating redundant synchronization messages. Preliminary results on several scientific benchmarks demonstrate that our approach is successful in minimizing the number of data and synchronization messages.

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Section: Optimizing Synchronizationmentioning

confidence: 82%

Section: Preliminary Resultsmentioning

confidence: 99%

Section: Optimizing Synchronizationmentioning

confidence: 99%

Section: Optimizing Synchronizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Minimizing data and synchronization costs in one-way communication

Kandemir

Shenoy²,

Banerjee³

et al.

Proceedings. 1998 International Conference on Parallel Processing (Cat. No.98EX205)

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Minimizing data and synchronization costs in one-way communication

Kandemir

Choudhary

Banerjee

et al. 2000

IEEE Trans. Parallel Distrib. Syst.

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AbstractÐMinimizing communication and synchronization costs is crucial to the realization of the performance potential of parallel computers. This paper presents a general technique which uses a global data-flow framework to optimize communication and synchronization in the context of the one-way communication model. In contrast to the conventional send/receive message-passing communication model, one-way communication is a new paradigm that decouples message transmission and synchronization. In parallel machines with appropriate low-level support, this may open up new opportunities not only to further optimize communication, but also to reduce the synchronization overhead. We present optimization techniques using our framework for eliminating redundant data communication and synchronization operations. Our approach works with the most general data alignments and distributions in languages like High Performance Fortran (HPF) and uses a combination of the traditional data-flow analysis and polyhedral algebra. Empirical results for several scientific benchmarks on a Cray T3E multiprocessor machine demonstrate that our approach is successful in reducing the number of data (communication) and synchronization messages, thereby reducing the overall execution times. memory machines, these techniques are readily applicable to uniform shared memory architectures as well. Gupta and Schonberg [19] show that compilers that generate code for one-way communication can exploit shared-memory architectures with flexible cache-coherence protocols (e.g., Wisconsin Typhoon [45] and Stanford FLASH [27]). To measure the benefits obtained from one-way communication (see Section 7), we used a Cray T3E [47] which is a logically shared, physically distributed memory multiprocessor that supports the PVM [15] and MPI [41] message-passing libraries, as well as a simple one-sided communication library provided by Silicon Graphics Inc.The Put primitiveÐexecuted by the producer of a dataÐtransfers the data from the producer's memory to the consumer's memory. This operation is very similar to the execution of a Send primitive by the producer and the execution of a matching Recv primitive by the consumer. There is an important difference, however: The consumer processor is not involved in the transfer directly and all the communication parameters are supplied by the producer [41]. As stated above, in order to ensure correctness, synchronization operations might be necessary. A large number of synchronization operations can be used to preserve the semantics of the program. These include barriers, point-to-point (or producer-consumer) synchronizations, and locks. The synchronization primitive used in this paper, namely SynchÐexecuted by the producer of a dataÐis a point-to-point communication primitive; however, our approach can be modified to work with other types of synchronizations as well. Note that both Stricker et al. [48] and Hayashi et al. [26] use barriers to implement synchronization. In contrast, our effort is aimed at reducing the tot...

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Synchronization elimination in the deposit model

Cited by 2 publications

References 9 publications

Minimizing data and synchronization costs in one-way communication

Minimizing data and synchronization costs in one-way communication

Minimizing data and synchronization costs in one-way communication

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