Donald Nguyen

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Several domain-specific languages (DSLs) for parallel graph analytics have been proposed recently. In this paper, we argue that existing DSLs can be implemented on top of a general-purpose infrastructure that (i) supports very fine-grain tasks, (ii) implements autonomous, speculative execution of these tasks, and (iii) allows application-specific control of(More)
For more than thirty years, the parallel programming community has used the <i>dependence graph</i> as the main abstraction for reasoning about and exploiting parallelism in "regular" algorithms that use dense arrays, such as finite-differences and FFTs. In this paper, we argue that the dependence graph is not a suitable abstraction for algorithms in new(More)
Speculative execution is a promising approach for exploiting parallelism in many programs, but it requires efficient schemes for detecting conflicts between concurrently executing threads. Prior work has argued that checking semantic commutativity of method invocations is the right way to detect conflicts for complex data structures such as kd-trees.(More)
Non-determinism in program execution can make program development and debugging difficult. In this paper, we argue that solutions to this problem should be on-demand, portable and parameterless. On-demand means that the programming model should permit the writing of non-deterministic programs since these programs often perform better than deterministic ones(More)
Performance tuning for data centers is essential and complicated. It is important since a data center comprises thousands of machines and thus a single-digit performance improvement can significantly reduce cost and power consumption. Unfortunately, it is extremely difficult as data centers are dynamic environments where applications are frequently released(More)
Many sparse matrix computations can be speeded up if the matrix is first reordered. Reordering was originally developed for direct methods but it has recently become popular for improving the cache locality of parallel iterative solvers since reordering the matrix to reduce bandwidth and wavefront can improve the locality of reference of sparse(More)
Irregular algorithms are organized around pointer-based data structures such as graphs and trees, and they are ubiquitous in applications. Recent work by the Galois project has provided a systematic approach for parallelizing irregular applications based on the idea of optimistic or speculative execution of programs. However, the overhead of optimistic(More)
Most client-side applications running on multicore processors are likely to be irregular programs that deal with complex, pointerbased data structures such as large sparse graphs and trees. However, we understand very little about the nature of parallelism in irregular algorithms, let alone how to exploit it effectively on multicore processors. In this(More)
Graph partitioning is a common and frequent preprocessing step in many high-performance parallel applications on distributedand shared-memory architectures. It is used to distribute graphs across memory and to improve spatial locality. There are several parallel implementations of graph partitioning for distributed-memory architectures. In this paper, we(More)