D. Culler, et al., Parallel Programming in Split-C,Supercomputing '93 Proceedings, 1993  Home/Search   Document Details and Download   Summary   Related Articles   Check

....to the same array element, the corresponding declared (or default) combine policy is applied, as in the scalar case. The various possibilities for the distribution of arrays in NestStep are inspired by other parallel programming languages for distributed memory systems, in particular by Split C [3]. Distributed shared arrays are declared as follows. The distribution may be either in contiguous blocks or cyclic. For instance, sh int[N] b; denotes a block wise distribution with block size dN=pe, where p is the size of the declaring group, and sh int[N] a; a a a a a b a 0 1 ....

....a cyclic distribution, where the owner of an array element is determined by its index modulo the group size. Such distributions for a 7 element array across a 4 processor group are shown in Figure 1. Multidimensional arrays can be distributed in up to three leftmost dimensions. As in Split C [3], there is for each multidimensional array A of dimensionality d a statically defined dimension k, 1 k d, k 3, such that all dimensions 1; k are distributed (all in the same manner, linearized row major) and dimensions k 1; d are not, i.e. A[i 1 ; i d ] is local to the ....

D. E. Culler, A. Dusseau, S. C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel Programming in Split-C. In Proc. Supercomputing'93, Nov. 1993.

....for the T3D following the shared memory model is simpler for most problems and is, therefore, the approach we followed in our work. This can be done using CRAFT, an extension of Fortran for the T3D which has several features in common with other languages for distributed shared memory machines [19, 25, 28]. CRAFT follows the Single Program Multiple Data(SPMD) model and contains a shared address space. In our experiments, each CRAFT process was allocated to a separate physical processor. Data objects can be declared as shared or private. Shared data can be distributed across memory using directives ....

....paradigm assumed by Polaris. Another reason is that PUT GET is rapidly gaining widespread acceptance. In fact, several portable shared memory programming models supporting PUT GET are already implemented on ordinary message passing machines, such as the IBM SP 1 2, Intel Paragon and TMC CM 5 [19, 25, 28]. Furthermore, several existing and newly proposed large scale machines, such as the T3E, directly support these primitives in hardware [21, 27] which reduces the effect of the increased communication overhead resulting from the extra data copy operations. Communication aggregation is useful in ....

D. Culler, et al., Parallel Programming in Split-C,Supercomputing '93 Proceedings, 1993

....program. 13 203 resul ts for our anal ysis running on the Java version of the Ol den Benchmarks [10, 9] BH: Impl ements the Barnes Hut N body sol er [3] BiSort:Impl ements bitonic sort [5] Em3d: Mode l the propagation of el2N romagnetic waves through objects in three dimensions [14]. Health: Simul9 es the Heal h care system in Col4 bia [26] MST:Computes the minimum spanning tree of a graph using Bentl ey sal9N40 hm [4] Perimeter: Computes the total perimeter of a region in a binary image represented by a quadtree [29] Power: Maximizes the economic ....

D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel programming in Split-C. In Proceedings of the ACM/IEEE Supercomputing Conference, pages 262 273, 1993.

....algorithm across different platforms. In fact, it seems to outperform all similar algorithms known to the authors on these platforms, and its performance is indifferent to the set of input distributions unlike previous efficient algorithms. The high level language used in our studies is SPLIT C [13], an extension of C for distributed memory machines. The algorithm makes use of MPI like communication primitives but does not make any assumptions as to how these primitives are actually implemented. The basic data transport is a read or write operation. The remote read and write typically have ....

....the cost of the irregular communication used by the most efficient algorithms. Hence, it is very important to perform an empirical evaluation of an algorithm using a wide variety of benchmarks, as we will do next. 4 Performance Evaluation Our sample sort algorithm was implemented using SPLIT C [13] and run on a variety of machines and processors, including the Cray Research T3D, the IBM SP 2 WN, and the Thinking Machines CM 5. For every platform, we tested our code on eight different benchmarks, each of which had both a 32 bit integer version (64 bit on the Cray T3D) and a 64 bit double ....

D.E. Culler, A. Dusseau, S.C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel programming in Split-C. In Proceedings of Supercomputing '93, ACM/IEEE, Portland, OR, 1993, pp. 262--273.

....to the set of input distributions, unlike previous efficient algorithms. However, unlike our randomized sorting algorithm, the performance and memory requirements of our regular sorting algorithm can be guaranteed with deterministically. The high level language used in our studies is SPLIT C [10], an extension of C for distributed memory machines. The algorithm makes use of MPI like communication primitives but does not make any assumptions as to how these primitives are actually implemented. The basic data transport is a read or write operation. The remote read and write typically have ....

....value as Splitter#k#) and push them onto U #i;k# . Step (9 ) Each processor P k merges the set of p consolidated subsequences #0;k# ;U #1;k# ; U #p,1;k# to produce the k column of the sorted array. 4 Performance Evaluation Our sample sort algorithm was implemented using SPLIT C [10] and run on a variety of machines and processors, including the Cray Research T3D, the IBM SP 2 WN, and the Thinking Machines CM 5. For every platform, we tested our code on nine different benchmarks, each of which had both a 32 bit integer version (64 bit on the Cray T3D) and a 64 bit double ....

D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel Programming in Split-C. In Proceedings of Supercomputing '93, pages 262--273, Portland, OR, November 1993.

....algorithm across different platforms. In fact, it seems to outperform all similar algorithms known to the authors on these platforms, and its performance is indifferent to the set of input distributions unlike previous efficient algorithms. The high level language used in our studies is Split C [13], an extension of C for distributed memory machines. The algorithm makes use of MPI like communication primitives but does not make any assumptions as to how these primitives are actually implemented. The basic data transport is a read or write operation. The remote read and write typically have ....

....the cost of the irregular communication used by the most efficient algorithms. Hence, it is very important to perform an empirical evaluation of an algorithm using a wide variety of benchmarks, as we will do next. 4. PERFORMANCE EVALUATION Our sample sort algorithm was implemented using Split C [13] and run on a variety of machines and processors, including the Cray Research T3D, the IBM SP 2 WN, and the Thinking Machines CM 5. For every platform, we tested our code on eight different benchmarks, each of which had both a 32 bit integer version (64 bit on the Cray T3D) and a 64 bit double ....

D. E. Culler, A. Dusseau, S. C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick, Parallel programming in Split-C," in Proceedings of Supercomputing '93, ACM#IEEE, Portland, OR, 1993, pp. 262#273.

....Only the inner loops are parallelized as no outer loop parallelism is available. Ten time steps are simulated and the size of the input vector is varied from 10 to 100 double precision floating point numbers. Benchmark Source Problem Size FFT Alewife [3] 4 128 complex doubles EM3D UC Berkeley [4] 6 30 node pairs MG Alewife [3] 64 2744 doubles CG Yeung [18] 27 1728 doubles EAR Spec92 [15] 10 100 doubles Table 7: Benchmark Summary. 4.2 Inner loop Parallelism In each of the benchmarks, inner loop parallelism is exploited by manually identifying independent expressions, function calls, ....

CULLER, D. E., DUSSEAU, m., GOLDSTEIN, S.C., KRISHNAMURTHY, m., LUMETTA, S., VON LIKEN, T., AND YELICK, K. Parallel programming in Split-C. In Supercomputing(November 1993), pp. 262-273.

....to the set of input distributions, unlike previous efficient algorithms. However, unlike our randomized sorting algorithm, the performance and memory requirements of our regular sorting algorithm can be guaranteed with probability one. The high level language used in our studies is Split C [10], an extension of C for distributed memory machines. The algorithm makes use of MPI like communication primitives but does not make any assumptions as to how these primitives are actually implemented. The basic data transport is a read or write operation. The remote read and write typically have ....

....cost of the irregular communication used by the most efficient algorithms. 7 Hence, it is very important to perform an empirical evaluation of an algorithm using a wide variety of benchmarks, as we will do next. 4 Performance Evaluation Our sample sort algorithm was implemented using Split C [10] and run on a variety of machines and processors, including the Cray Research T3D, the IBM SP 2 WN, and the Thinking Machines CM 5. For every platform, we tested our code on nine different benchmarks, each of which had both a 32 bit integer version (64 bit on the Cray T3D) and a 64 bit double ....

D.E. Culler, A. Dusseau, S.C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel Programming in Split-C. In Proceedings of Supercomputing '93, pages 262-- 273, Portland, OR, November 1993.

....the motion of water molecules in three dimensional space. Two applications come from the suite of benchmarks used by Lai Falsafi in their work on speculation in hardware DSMs [12] Em3d is a shared memory implementation of the Split C program to perform 3D modeling of electromagnetic waves [7], and tomcatv is a shared memory implementation of the mesh generation program from the SPEC92 floating point benchmark suite. The final application is fft, a three dimensional FFT kernel from the NAS parallel benchmarks [3] The version we use comes from the Treadmarks application suite [2] ....

D. E. Culler, A. C. Arpaci-Dusseau, S. C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. A. Yelick. Parallel programming in split-c. In Supercomputing, pages 262--273, 1993. 25

....the experimental study strategy. Section 4 presents the experimental measurements, followed by conclusions in section 5. 2. AN OVERVIEW OF UPC History and Status UPC, or Unified Parallel C, builds on the experience gained from its predecessor distributed shared memory C languages such as Split C[Cul93], AC[Car99] and PCP[Bro95] UPC maintains the C philosophy by keeping the language concise, expressive, and by giving the programmer the power of getting closer to the hardware. These UPC features have gained a great deal of interest from the community. Therefore, support for UPC is consistently ....

Culler, David E., Andrea Dusseau, Seth Copen Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thorsten von Eicken, and Katherine Yelick, "Parallel Programming in Split-C," in Proceedings of Supercomputing`93, Portland, OR, November 15-19, 1993, pp. 262-273.

....the program. 13 We present results for our analysis running on the Java version of the Olden Benchmarks [10, 9] # BH: Implements the Barnes Hut N body solver [3] # BiSort: Implements bitonic sort [5] # Em3d: Models the propagation of electromagnetic waves through objects in three dimensions [14]. # Health: Simulates the Health care system in Colombia [26] # MST: Computes the minimum spanning tree of a graph using Bentley s algorithm [4] # Perimeter: Computes the total perimeter of a region in a binary image represented by a quadtree [29] # Power: Maximizes the economic eciency of ....

D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel programming in Split-C. In Proceedings of the ACM/IEEE Supercomputing Conference, pages 262-273, 1993.

.... sion of the Olden Benchmarks [6, 5] This benchmark set contains the following applications: bh: An implementation of the Barnes Hut N body solver [2] bisort: An implementation of bitonic sort [4] em3d: Models the propagation of electromagnetic waves through objects in three dimensions [8]. health: Simulates the health care system in Colombia [15] mst: Computes the minimum spanning tree of a graph using Bentley s algorithm [3] perimeter: Computes the total perimeter of a region in a binary image represented by a quadtree [17] power: Maximizes the economic eciency of ....

D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel programming in Split-C. In Proceedings of the ACM/IEEE

....an interface that is similar to that of Shared Regions [83] Another similar project is Cid [76] which also extends C to provide an additional global name space. The Cid project is more ambitious than CRL, as it also provides a multithreading and synchronization model. The Split C language [31] is another recent language that extends C with a global address space. It di ers from other languages in that arithmetic can be performed on global pointers. The Midway system [11] supports a global address space by requiring that the programmer correctly associate synchronization with every ....

D.E. Culler, A. Dusseau, S.C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. "Parallel Programming in Split-C". In Proceedings of Supercomputing '93, pages 262--273, Portland, OR, November 1993.

....get associated with the operations sent by the above sender. The SPARC V9 MEMBAR instruction provides more (but not complete) flexibility in specifying the operations that need to be ordered by the MEMBAR. The Split C mechanism. Split C allows a counter to be associated with every operation [15]. It further defines a sync instruction that is also associated with a counter, and waits for all preceding (by program order) operations associated with the same counter to complete. This provides a mechanism to associate a sender with operations it sends (by preceding the sender with a sync ....

D. E. Culler, A. Dusseau, S. C. Golstein, A. Krishnamurthy, T. von Eicken, and K. Yelick. Parallel Programming in Split-C. In Proc. Supercomputing '93, pages 262--273, Portland, November 1993.

....than a cluster of multiprocessors. Distributed Shared Memory environments (DSM) provide another alternative to unify the memory models, as presented in [9] 8] 3] Their performance relies mainly upon the efficiency of the memory consistency protocol. Two other approaches can be noted. Split C [2] is a parallel extension to C that supports access to a global address space on distributed memory architectures. Recently, OpenMP [5] has been implemented on a network of workstations on top of the Treadmark DSM system. Again, its performance relies on the shared virtual memory software. The HMM ....

D.E. Culler et al. Parallel programming in Split-C. In 1EEE, editor, Proceedings, Supercomputing '93: Portland, Oregon, November 15-19, 1993.

....a CLUMP. Shared virtual memory environments (DSVM) provide another alternative to unify the memory models, as presented in [5] 6] 7] 8] Performance of the DSVM model mainly relies on the efficiency of the memory consistency protocol that is used. Two other approaches can be noticed. Split C [9] is a parallel extension to C that supports access to a global address space on distributed memory architectures. Recently, OpenMP [10] has been implemented on a network of workstations on top of the Treadmark DSM system. Again, its performance relies on the shared virtual memory software. The ....

David E. Culler, Andrea Dusseau, Seth Copen Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thomten von Eicken, and Katherine Yelick. Parallel programming in Split-C. In IEEE, editor, Proceedings, Supercomputing '93: Portland, Oregon November 15 19, 1993.

....[9] and SoftFLASH [10] As for the monoprocessor platforms, the performance of DVSM mainly relies on the protocol efficiency. Other programming paradigms, initially developed in the context of the single processor node parallel computers, may also unify the memory model for the CLUMP. Split C [11] is a parallel extension to C programming language that support access to a global address space on distributed memory architectures. Split C allows the programmer to use two types of pointers: standard and global pointers. Standard pointers refere to only a memory region local to the processor ....

David E. Culler, Andrea Dusseau, Seth Copen Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thorsten von Eicken, and Katherine Yelick. Parallel programming in Split-C. In IEEE, editor, Proceedmgs, Supereomputmg '93: Portlad, Oregon,, November 15 19, 1993.

....they lack scoping and a sense of locality. This is partly due to the fact that these languages support a contentaddressable associative memory, rather than a traditional location addressable shared memory as in ParC. Split C is another language that supports a non traditional shared memory model [36, 37]. Access to the shared address space is mediated by active messages, and speci cally asynchronous put and get of memory blocks to and from other PEs. The accessing PE can then check (of busy wait) on a local ag to see when the put or get completes. Hence assignment from a remote location is ....

....the context of the same task share that task s address space. However they cannot access the address spaces of other tasks. In addition, accessibility is not necessarily correlated with locality. Split C exposes the locality by providing special operations (put and get) to access remote memory [36, 37]. A number of systems go a step further and introduce special annotations that tag variables as private or shared [48, 49] This is useful as an indication of where the variable should be stored in a NUMA architecture. For example, on the Cedar system the degree of sharing dictates whether a ....

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D. E. Culler, A. Dusseau, S. C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick, `Parallel programming in Split-C'. In Supercomputing '93, 262-273 (1993).

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David E. Culler, Andrea Dusseau, Seth Copen Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thorsten von Eicken, and Katherine Yelick. Parallel Programming in Split-C. In Proceedings of Supercomputing '93, pages 262--273, 1993.

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D. E. Culler, A. Dusseau, S. C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, K. Yelick, "Parallel Programming in Split-C," Proceedings of Supercomputing '93, Portland, Oregon, November 1993, pp. 262-273.

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D. E. Culler, A. Dusseau, S. C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel programming in Split-C. In Supercomputing '93, pages 262--273, Portland, Oregon, November 1993.

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David E. Culler, Andrea Dusseau, Seth C. Goldstein, Arvind Krishnamurthy, Steve Lumetta, Thorsten von Eicken, and Kathy Yelick. Parallel Programming in Split-C. In Proceedings of Supercomputing'93 (Portland, OR, November 1993).

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D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. Eicken, and K. Yelick. Parallel programming in Split-C. In Supercomputing (SC1993.

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D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. Eicken, and K. Yelick. Parallel programming in Split-C. In Supercomputing (SC1993.

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D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. Eicken, and K. Yelick. Parallel programming in Split-C. In Supercomputing (SC1993.

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David E. Culler, Andrea Dusseau, Seth C. Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thorsten von Eicken, and Katherine Yelick. Parallel Programming in Split-C. In Proceedings of Supercomputing '93, pages 262--273, 1993.

....shared data. In effect, these languages offer only global shared and local mixed. Although private data exists at run time, the static type system cannot identify it in a useful manner, and many of the clients listed in Section 2.2 cannot be deployed. Also in the C family, CC [12] and Split C [14] do not directly address the notion of private data. This may mean that all data is presumed shared, but it is difficult to know exactly what semantics are required, especially with regard to code reordering and other aggressive optimizations. Cilk explicitly treats all memory as shared, and ....

D. E. Culler, A. Dusseau, S. C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel programming in Split-C. In IEEE, editor, Proceedings, Supercomputing '93: Portland, Oregon, November 15--19, 1993, pages 262--273, 1109 Spring Street, Suite 300, Silver Spring, MD 20910, USA, 1993. IEEE Computer Society Press.

....2.2.19 and uses the ext2 file system and Linux RAID software. Jobs are started in parallel using our own software, ice, described in more detail in the next section. Our sorting code also depends on two pieces of research software: Split C and a UDP based implementation of Active Messages. Split C [6] is a parallel extension to C that supports efficient access to a global address space on distributed memory machines. For communication, Split C uses Active Messages [9] a restricted, lightweight version of remote procedure calls. Since Active Messages in our cluster is layered upon UDP [4] the ....

David E. Culler, Andrea Dusseau, Seth Copen Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thorsten von Eicken, and Katherine Yelick. Parallel Programming in Split-C. In Proceedings of Supercomputing '93, pages 262--273, 1993.

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D. Culler, et al., Parallel Programming in Split-C,Supercomputing '93 Proceedings, 1993

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Culler, D. E., Dusseau, A., Goldstein, S. C., Krishnamurthy, A., Lumetta, S., von Eicken, T., and Yelick, K. (1993a). Parallel Programming in SplitC. In Proc. of Supercomputing '93.

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CULLER, D. E., DUSSEAU, A. C., GOLDSTEIN, S. C., KRISHNAMURTHY, A., LUMETTA, S., VON EICKEN, T., AND YELICK, K. Parallel Programming in Split-C. In Proceedings of Supercomputing '93 (1993), pp. 262--273.

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D. Culler, A. Dusseau, S. Goldstein, S. Lumetta, T. von Eicken, and K. Yelick. Parallel programming in Split-C. Proceedings of Supercomputing '93, pp. 262--273, Nov. 1993.

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E. Culler, A. Dusseau, S. Goldstein, A. Krishna-murthy, S. Lumetta, T. Eicken, and K. Yelick, "Parallel programming in Split C," in proceedings of Proc. Supercomputing, 1993.

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David E. Culler, Andrea Dusseau, Seth Copen Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thorsten von Eiken, and Katherine Yelick. Parallel programming in split-C. International Conference on Supercomputing, November 1993.

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D. E. Culler, A. Dusseau, S. C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel programming in Split-C. In IEEE, editor, Proceedings, Supercomputing '93: Portland, Oregon, November 15--19, 1993.

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CULLER, D. E., DUSSEAU, A. C., GOLDSTEIN, S. C., KRISHNAMURTHY, A., LUMETTA, S., VON EICKEN, T., AND YELICK, K. Parallel Programming in Split-C. In Proceedings of Supercomputing '93 (1993), pp. 262--273.

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David E. Culler, Andrea Dusseau, Seth Copen Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thorsten von Eicken, and Katherine Yelick. Parallel programming in split-c. In Proceedings of the Supercomputing '93 Conference, Nov. 1993.

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D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken and K. Yelick, "Parallel Programming in Split-C", Supercomputing `93, Portland OR. Nov. 1993. IEEE Computer Society Press.

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D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. "Parallel Programming in Split-C." In Supercomputing '93, Portland, OR, Nov. 1993.

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D. E. Culler et al. Parallel Programming in Split-C. In Supercomputing '93, Portland, Oregon, November 1993.

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D. E. Culler, A. Dusseau, S. C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel programming in Split-C. In IEEE, editor, Proceedings, Supercomputing '93: Portland, Oregon, November 15--19, 1993.

No context found.

D.E. Culler, A. Dusseau, S.C. Goldstein, A. Krishnamurthy, S. Lumetta, T. von Eicken, and K. Yelick. Parallel Programming in Split-C. In Proc. Supercomputing, 1993.

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David E. Culler, Andrea Dusseau, Seth Copen Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thorsten von Eiken, and Katherine Yelick. Parallel programming in split-C. International Conference on Supercomputing, November 1993.

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Culler, David E., Andrea Dusseau, Seth Copen Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thorsten von Eicken, and Katherine Yelick, "Parallel Programming in Split-C," in Proceedings of Supercomputing`93, Portland, OR, November 15-19, 1993, pp. 262-273.

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D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. Eicken, and K. Yelick. Parallel Programming in Split-C. Supercomputing '93, pages 262--273, November 1993.

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D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. Eicken, and K. Yelick. Parallel Programming in Split-C. Proceedings of Supercomputing '93, pages 262--273, November 1993.

No context found.

D. Culler, et al., Parallel Programming in Split-C,Supercomputing '93 Proceedings, 1993

No context found.

D. Culler, A. Dusseau, S. Goldstein, A. Krishnamurthy, S. Lumetta, T. Eicken, and K. Yelick. Parallel Programming in Split-C. Proceedings of Supercomputing '93, pages 262--273, November 1993.

No context found.

David E. Culler, Andrea Dusseau, Seth C. Goldstein, Arvind Krishnamurthy, Steve Lumetta, Thorsten von Eicken, and Katherine Yelick. Parallel Programming in Split-C. In Proceedings of Supercomputing '93, pages 262--273, 1993.

No context found.

David E. Culler, Andrea Dusseau, Seth Copen Goldstein, Arvind Krishnamurthy, Steven Lumetta, Thorsten von Eicken, and Katherine Yelick. Parallel programming in Split-C. In IEEE, editor, Proceedmgs, Supereomputmg '93: Portlad, Oregon,, November 15 19, 1993.

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