![]() ![]() It is based on the Asynchronous Partitioned Global Address Space (APGAS) programming model, supporting the same fine-grained concurrency mechanisms within and across shared-memory nodes. X10 is a high-performance, high-productivity programming language aimed at large-scale distributed and shared-memory parallel applications. Our results show that the compiler transformation results in speedups from 1.15X up to 21X compared with the baseline versions and that they achieve up to 63% the performance of the MPI versions. The performance evaluation uses two microbenchmarks and three benchmarks to obtain scaling and absolute performance numbers on up to 32768 cores of a Power 775 machine. Larger messages increase the network efficiency and static coalescing decreases the overhead of library calls. This paper presents an optimization for the Unified Parallel C language that combines compile time (static) and runtime (dynamic) coalescing of shared data, without the knowledge of physical data mapping. On the other hand, most compiler optimizations of fine-grain accesses require knowledge of physical data mapping and the use of parallel loop constructs. The downside of manual code transformations is the increased program complexity that hinders programmer productivity. Manual code transformations or compiler optimizations are required to improve the performance of programs with fine-grained accesses. However, PGAS programs may have many fine-grained shared accesses that lead to performance degradation. The goal of Partitioned Global Address Space (PGAS) languages is to improve programmer productivity in large scale parallel machines. A performance evaluation, using up to 2048 cores of a POWER 775 supercomputer, allows for a prediction that applications with regular accessesĬan achieve up to 180\% of the performance of hand-optimized versions while applications with irregular accesses yield performance gain from 1.12X up to 6.3X speedup. This paper introduces a shared-data localization transformation based on linear memory descriptors (LMADs) that reduces the amount of instrumentation introduced by the compiler into programs written in the UPC language and describes a prototype implementation of the proposed transformation. Result in excessive instrumentation that hinders performance. A straightforward implementation of the inspector-executor in a PGAS system may One solution is to use the inspector-executor technique to determine which accesses are indeed remote and which accesses may be coalesced in larger remote access operations. However, PGAS programs may have fine-grained shared accesses that lead to performance degradation. ![]() However, the compiler have to create the communication mechanisms and the runtime system to use synchronization primitives to ensure the correct execution of the programs. ![]() Programs written in Partitioned Global Address Space (PGAS) languages can access any location of the entire address space via standard read/write operations. This evaluation indicates that the compiler transformation results in speedups between 1.15 x and 21 x over a baseline and that these automated transformations achieve up to 63 percent the performance of the MPI versions. A performance evaluation reports both scaling and absolute performance numbers on up to 32,768 cores of a Power 775 supercomputer. In this paper, a novel application of the inspector-executor model overcomes these limitations and allows profitable code transformations, which result in fewer and larger messages sent through the network, when neither the data mapping nor the number of processing nodes are known at compilation time. Until now code transformations to PGAS programs have been restricted to the cases where both the physical mapping of the data or the number of processing nodes are known at compilation time. When the data is distributed to remote computing nodes, code transformations are required to prevent performance degradation. These languages allow fine-grained communication and lead to programs that perform many fine-grained accesses to data. This paper addresses important limitations in the code generation for partitioned global address space (PGAS) languages. Significant progress has been made in the development of programming languages and tools that are suitable for hybrid computer architectures that group several shared-memory multicores interconnected through a network. ![]()
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