The cost of data movement has always been an important concern in high performance computing (HPC) systems. It has now become the dominant factor in terms of both energy consumption and performance. Support for expression of data locality has been explored in the past, but those efforts have had only modest success in being adopted in HPC applications for various reasons. them However, with the increasing complexity of the memory hierarchy and higher parallelism in emerging HPC systems, locality management has acquired a new urgency. Developers can no longer limit themselves to low-level solutions and ignore the potential for productivity and performance portability obtained by using locality abstractions. Fortunately, the trend emerging in recent literature on the topic alleviates many of the concerns that got in the way of their adoption by application developers. Data locality abstractions are available in the forms of libraries, data structures, languages and runtime systems; a common theme is increasing productivity without sacrificing performance. This paper examines these trends and identifies commonalities that can combine various locality concepts to develop a comprehensive approach to expressing and managing data locality on future large-scale high-performance computing systems.

Parallel machines are becoming more complex with increasing core counts and more heterogeneous architectures. However, the commonly used parallel programming models, C/C++ with MPI and/or OpenMP, make it difficult to write source code that is easily tuned for many targets. Newer language approaches attempt to ease this burden by providing optimization features such as automatic load balancing, overlap of computation and communication, message-driven execution, and implicit data layout optimizations. In this paper, we compare several implementations of LULESH, a proxy application for shock hydrodynamics, to determine strengths and weaknesses of different programming models for parallel computation. We focus on four traditional (OpenMP, MPI, MPI+OpenMP, CUDA) and four emerging (Chapel, Charm++, Liszt, Loci) programming models. In evaluating these models, we focus on programmer productivity, performance and ease of applying optimizations.

Scientists across a wide range of domains increasingly rely on computer simulation for their investigations. Such simulations often spend a majority of their run-times solving large systems of linear equations that require vast amounts of computational power and memory. It is hence critical to design solvers in a highly efficient and scalable manner. Hypre is a high performance, scalable software library that offers several optimized linear solver routines and pre-conditioners. In this paper, we study the characteristics of Hypre's Preconditioned Conjugate Gradient (PCG) solver algorithm. The PCG routine is known to spend a majority of its communication time in the MPI All reduce operation to compute a global summation during the inner product operation. The MPI All reduce is a blocking operation, whose latency is often a limiting factor to the overall efficiency of the PCG solver routine, and correspondingly the performance of simulations that rely on this solver. Hence, hiding the latency of the MPI All reduce operation is critical towards scaling the PCG solver routine and improving the performance of many simulations. The upcoming revision of MPI, MPI-3, will provide support for non-blocking collective communication to enable latency-hiding. The latest Infini Band adapter from Mellanox, ConnectX-2, enables offloading of generalized lists of communication operations to the network interface. Such an interface can be leveraged to design non-blocking collective operations. In this paper, we design fully functional, scalable algorithms for the MPI Iall reduce operation, based on the network offload technology. To the best of our knowledge, these network offload-based algorithms are the first to be presented for the MPI Iall reduce operation. Our designs scale beyond 512 processes and we achieve near perfect communication/computation overlap. We also re-design the PCG solver routine to leverage our proposed MPI Iall reduce operation to hide the latency of the global reduction operations. We observe up to 21% improvements in the run-times of the PCG routine, when compared to the default PCG implementation in Hypre. We also note that about 16% of the overall benefits are due to overlapping the All reduce operations.

Over the last decade, InfiniBand has become an increasingly popular interconnect for deploying modern supercomputing systems. However, there exists no detection service that can discover the underlying network topology in a scalable manner and expose this information to runtime libraries and users of the high performance computing systems in a convenient way. In this paper, we design a novel and scalable method to detect the InfiniBand network topology by using Neighbor-Joining techniques (NJ). To the best of our knowledge, this is the first instance where the neighbor joining algorithm has been applied to solve the problem of detecting InfiniBand network topology. We also design a network-topology-aware MPI library that takes advantage of the network topology service. The library places processes taking part in the MPI job in a network-topology-aware manner with the dual aim of increasing intra-node communication and reducing the long distance inter-node communication across the InfiniBand fabric.