Languages and Compilers for Parallel Computing: 29th by Chen Ding, John Criswell, Peng Wu

By Chen Ding, John Criswell, Peng Wu

This booklet constitutes the completely refereed post-conference lawsuits of the twenty ninth overseas Workshop on Languages and Compilers for Parallel Computing, LCPC 2016, held in Rochester, manhattan, united states, in September 2016.

The 20 revised complete papers offered including four brief papers have been rigorously reviewed. The papers are equipped in topical sections on huge scale parallelism, resilience and endurance, compiler research and optimization, dynamic computation and languages, GPUs and personal reminiscence, and runt-time and function analysis.

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Extra info for Languages and Compilers for Parallel Computing: 29th International Workshop, LCPC 2016, Rochester, NY, USA, September 28-30, 2016, Revised Papers

Sample text

For abstracting the MPI operations, we group MPI operations issued from an ATS node of a process into an equivalence class. Our abstraction differentiates the MPI operations issued by different processes, in different locations in the code, which allows ParFuse to compute more precise matchings than previous approaches. , specific to each process) for the buffers of the MPI operations. MPI Matching. , their equivalence classes, is that they must be matched following the out-of-order matching semantics of the MPI.

Theorem 3. At the end of the first superstep, all reachable vertices will propagate a distance at most i−1 d0 (v)−1 (1−τ )j j=0 (1−τ )d0 (v) (1−τ ) j . Proof. Let W (i) = j=0 denote the length of the longest path that will (1−τ )i be tolerated by a vertex of true distance i without triggering a propagation. 46 A. Fidel et al. Lemma 4 shows that this holds for vertices with true distance 1. Assume that this property holds for vertices of distance i. Let v be a vertex with true distance i + 1 discovered along some path π.

Message passing semantics are simulated by performing the analysis on the pCFG. To precisely match MPI operations in pCFG, the analysis would first block on corresponding MPI operations and the symbolic constraints on the target expression of a send must isomorphically match the symbolic constraints on the target expression of a receive operation. While scalable, this approach makes matching difficult when complex abstractions are used to describe the equivalence classes and target expressions evaluating to multiple values.

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