this paper investigates the compilation of a committed-choice rulebased language, Constraint Handling Rules (CHR), to specialized hardware circuits. the developed hardware is able to turn the intrinsic concurrency of ...
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Task-Oriented programming (TOP) is a novel programming paradigm for the construction of distributed systems where users work together on the *** multiple users collaborate, they need to interact with each other freque...
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the proliferation of data in graph form calls for the development of scalable graph algorithms that exploit parallel processing environments. One such problem is the computation of a graph's minimum spanning fores...
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the proliferation of data in graph form calls for the development of scalable graph algorithms that exploit parallel processing environments. One such problem is the computation of a graph's minimum spanning forest (MSF). Past research has proposed several parallel algorithms for this problem, yet none of them scales to large, high-density graphs. In this paper we propose a novel, scalable, parallel MSF algorithm for undirected weighted graphs. Our algorithm leverages Prim's algorithm in a parallel fashion, concurrently expanding several subsets of the computed MSF. Our effort focuses on minimizing the communication among different processors without constraining the local growth of a processor's computed subtree. In effect, we achieve a scalability that previous approaches lacked. We implement our algorithm in CUDA, running on a GPU and study its performance using real and synthetic, sparse as well as dense, structured and unstructured graph data. Our experimental study demonstrates that our algorithm outperforms the previous state-of-the-art GPU-based MSF algorithm, while being several order of magnitude faster than sequential CPU-based algorithms.
We introduce our major ideas of a wait-free, linearizable, and disjoint-access parallel NCAS library, called RTNCAS. It focuses the construction of wait-free data structure operations (DSO) in real-time circumstances....
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ISBN:
(纸本)9781450301190
We introduce our major ideas of a wait-free, linearizable, and disjoint-access parallel NCAS library, called RTNCAS. It focuses the construction of wait-free data structure operations (DSO) in real-time circumstances. RTNCAS is able to conditionally swap multiple independent words (NCAS) in an atomic manner. It allows us, furthermore, to implement arbitrary DSO by means of their sequential specification.
A variety of programming models exist to support large-scale, distributed memory, parallel computation. these programming models have historically targeted coarse-grained applications with natural locality such as tho...
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ISBN:
(纸本)9781450301190
A variety of programming models exist to support large-scale, distributed memory, parallel computation. these programming models have historically targeted coarse-grained applications with natural locality such as those found in a variety of scientific simulations of the physical world. Fine-grained, irregular, and unstructured applications such as those found in biology, social network analysis, and graph theory are less well supported. We propose Active Pebbles, a programming model which allows these applications to be expressed naturally;an accompanying execution model ensures performance and scalability.
We describe two novel constructs for programmingparallel machines with multi-level memory hierarchies: call-up, which allows a child task to invoke computation on its parent, and spawn, which spawns a dynamically det...
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ISBN:
(纸本)9781450301190
We describe two novel constructs for programmingparallel machines with multi-level memory hierarchies: call-up, which allows a child task to invoke computation on its parent, and spawn, which spawns a dynamically determined number of parallel children until some termination condition in the parent is met. Together we show that these constructs allow applications with irregular parallelism to be programmed in a straightforward manner, and furthermore these constructs complement and can be combined with constructs for expressing regular parallelism. We have implemented spawn and call-up in Sequoia and we present an experimental evaluation on a number of irregular applications.
this paper evaluates features of graph coloring algorithms implemented on graphics processing units (GPUs), comparing coloring heuristics and thread decompositions. As compared to prior work on graph coloring for othe...
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ISBN:
(纸本)9781450301190
this paper evaluates features of graph coloring algorithms implemented on graphics processing units (GPUs), comparing coloring heuristics and thread decompositions. As compared to prior work on graph coloring for other parallel architectures, we find that the large number of cores and relatively high global memory bandwidth of a GPU lead to different strategies for the parallel implementation. Specifically, we find that a simple uniform block partitioning is very effective on GPUs and our parallel coloring heuristics lead to the same or fewer colors than prior approaches for distributed-memory cluster architecture. Our algorithm resolves many coloring conflicts across partitioned blocks on the GPU by iterating through the coloring process, before returning to the CPU to resolve remaining conflicts. Withthis approach we get as few color (if not fewer) than the best sequential graph coloring algorithm and performance is close to the fastest sequential graph coloring algorithms which have poor color quality.
the Toolkit for Accurate Scientific Software (TASS) is a suite of tools for the formal verification of MPI-based parallel programs used in computational science. TASS can verify various safety properties as well as co...
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ISBN:
(纸本)9781450301190
the Toolkit for Accurate Scientific Software (TASS) is a suite of tools for the formal verification of MPI-based parallel programs used in computational science. TASS can verify various safety properties as well as compare two programs for functional equivalence. the TASS front end takes an integer n >= 1 and a C/MPI program, and constructs an abstract model of the program with n processes. Procedures, structs, (multi-dimensional) arrays, heap-allocated data, pointers, and pointer arithmetic are all representable in a TASS model. the model is then explored using symbolic execution and explicit state space enumeration. A number of techniques are used to reduce the time and memory consumed. A variety of realistic MPI programs have been verified with TASS, including Jacobi iteration and manager-worker type programs, and some subtle defects have been discovered. TASS is written in Java and is available from http://***/tass under the Gnu Public License.
Modern parallel microprocessors deliver high performance on applications that expose substantial fine-grained data parallelism. Although data parallelism is widely available in many computations, implementing data par...
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ISBN:
(纸本)9781450301190
Modern parallel microprocessors deliver high performance on applications that expose substantial fine-grained data parallelism. Although data parallelism is widely available in many computations, implementing data parallel algorithms in low-level languages is often an unnecessarily difficult task. the characteristics of parallel microprocessors and the limitations of current programming methodologies motivate our design of Copperhead, a high-level data parallel language embedded in Python. the Copperhead programmer describes parallel computations via composition of familiar data parallel primitives supporting both flat and nested data parallel computation on arrays of data. Copperhead programs are expressed in a subset of the widely used Python programming language and interoperate with standard Python modules, including libraries for numeric computation, data visualization, and analysis. In this paper, we discuss the language, compiler, and runtime features that enable Copperhead to efficiently execute data parallel code. We define the restricted subset of Python which Copperhead supports and introduce the program analysis techniques necessary for compiling Copperhead code into efficient low-level implementations. We also outline the runtime support by which Copperhead programs interoperate with standard Python modules. We demonstrate the effectiveness of our techniques with several examples targeting the CUDA platform for parallelprogramming on GPUs. Copperhead code is concise, on average requiring 3.6 times fewer lines of code than CUDA, and the compiler generates efficient code, yielding 45-100% of the performance of hand-crafted, well optimized CUDA code.
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