The proceedings contain 6 papers. The topics discussed include: llvm-based communication optimizations for PGAS programs;llvm parallel intermediate representation: design and evaluation using OpenSHMEM communications;...
ISBN:
(纸本)9781450340052
The proceedings contain 6 papers. The topics discussed include: llvm-based communication optimizations for PGAS programs;llvm parallel intermediate representation: design and evaluation using OpenSHMEM communications;MPI-Checker - static analysis for MPI;FITL: extending llvm for the translation of fault-injection directives;integrating GPU support for OpenMP offloading directives into clang;and SKA - static kernel analysis using llvm IR.
MUST, a dynamic MPI correctness checker, is extended with a type and memory allocation tracking sanitizer called TypeART for C/C++ codes based on the llvmcompiler framework. The sanitizer extracts type information an...
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ISBN:
(纸本)9781728102269
MUST, a dynamic MPI correctness checker, is extended with a type and memory allocation tracking sanitizer called TypeART for C/C++ codes based on the llvmcompiler framework. The sanitizer extracts type information and inserts instrumentation to track memory allocations and deallocations relevant to MPI communication. This allows MUST to check for type compatibility between the type-less communication buffer and the declared MPI datatype at all phases of the MPI communication, namely message assembly, message transfer and message disassembly into the receiving buffer. We evaluate our approach on benchmarks taken from SPEC MPI 2007 and two CORAL mini applications. The results show that our approach typically exhibits acceptable runtime and memory overheads. In particular, the memory overhead was below 20% in all cases.
Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is often a barrier when scaling to larger data se...
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ISBN:
(纸本)9781450340052
Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is often a barrier when scaling to larger data sets. This paper presents a just-in-time compiler for Python that focuses in scientific and array-oriented computing. Starting with the simple syntax of Python, Numba compiles a subset of the language into efficient machine code that is comparable in performance to a traditional compiled language. In addition, we share our experience in building a JIT compiler using llvm[1]. Copyright is held by the owner/author(s). Publication rights licensed to ACM.
The llvm community is currently developing OpenMP 4.1 support, consisting of software improvements for Clang and new runtime libraries. OpenMP 4.1 includes offloading constructs that permit execution of user selected ...
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