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The goal of Partitioned Global Address Space (PGAS) languages is to improve programmer productivity in large scale parallel machines. However, PGAS programs may have many fine-grained shared accesses that lead to performance degradation. Manual code transformations or compiler optimizations are required to improve the performance of programs with fine-grained accesses. The downside of manual code transformations is the increased program complexity that hinders programmer productivity. On the other hand, most compiler optimizations of fine-grain accesses require knowledge of physical data mapping and the use of parallel loop constructs. 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. Larger messages increase the network efficiency and static coalescing decreases the overhead of library calls. 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. 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.

Programming for large-scale, multicore-based architectures requires adequate tools that offer ease of programming while not hindering application performance. StarSs is a family of parallel programming models based on automatic function level parallelism that targets productivity. StarSs deploys a data-flow model: it analyses dependencies between tasks and manages their execution, exploiting their concurrency as much as possible. We introduce Cluster Superscalar (ClusterSs), a new StarSs member designed to execute on clusters of SMPs. ClusterSs tasks are asynchronously created and assigned to the available resources with the support of the IBM APGAS runtime, which provides an efficient and portable communication layer based on one-sided communication.This short paper gives an overview of the ClusterSs design on top of APGAS, as well as the conclusions of a productivity study; in this study, ClusterSs was compared to the IBM X10 language, both in terms of programmability and performance. A technical report is available with the details.

Scalability to large number of processes is one of the weaknesses of current MPI implementations. Standard implementations are able to scale to hundreds of nodes, but not beyond. The main problem in these implementations is that they assume some resources (for both data and control-data) will always be available to receive/process unexpected messages. As we will show, this is not always true, especially in short-memory machines like the BG/L that has 64K nodes but each node only has 512Mbytes of memory.The objective of this paper is to present one algorithm that improves the robustness of MPI implementations for short-memory MPPs, taking care of data and control-data reception, the system will scale up to any number of nodes. The proposed solution achieves this goal without any observable overhead when there are no memory problems. Furthermore, in the worst case, when memory resources are extremely scarce, the overhead will never double the execution time (and we should never forget that in this extreme situation, traditional MPI implementations would fail to execute).

The construction of suffix tree for very long sequences is essential for many applications, and it plays a central role in the bioinformatic domain. With the advent of modern sequencing technologies, biological sequence databases have grown dramatically. Also the methodologies required to analyze these data have become everyday more complex, requiring fast queries to multiple genomes. In this paper we presented Parallel Continuous Flow PCF, a parallel suffix tree construction method that is suitable for very long strings. We tested our method on the construction of suffix tree of the entire human genome, about 3GB. We showed that PCF can scale gracefully as the size of the input string grows. Our method can work with an efficiency of 90% with 36 processors and 55% with 172 processors. We can index the Human genome in 7 minutes using 172 nodes.