PPOPP

In this paper we unveil some energy efficiency and performance frontiers for sparse computations on GPU-based supercomputers. To do this, we consider state-of-the-art implementations of the sparse matrix-vector (SpMV) product in libraries like cuSPARSE, ...

Power and energy efficiency is one of the major challenges to achieve exascale computing in the next several years. While chips operating at low voltages have been studied to be highly energy-efficient, low voltage operations lead to heterogeneity ...

In the last few years, managed runtime environments such as the Java Virtual Machine (JVM) are increasingly used on large-scale multicore servers. The garbage collector (GC) represents a critical component of the JVM and has a significant influence on ...

The Bulk Synchronous Parallel programming model is showing performance limitations at high processor counts. We propose over-decomposition of the domain, operated on as tasks, to smooth out utilization of the computing resource, in particular the node ...

As the attention given to big data grows, cluster computing systems for distributed processing of large data sets become the mainstream and critical requirement in high performance distributed system research. One of the most successful system is Hadoop ...

High performance requirements increased the popularity of unconventional architectures. While providing better performance, such architectures are generally harder to program and generate code for. In this paper, we present our approach to ease ...

In the multicore era it was possible to exploit the increase in on-chip parallelism by simply running multiple MPI processes per chip. Unfortunately, manycore processors' greatly increased thread- and data-level parallelism coupled with a reduced memory ...

Graphics Processing Unit (GPU) shows stunning computing power for scientific applications in the past few years, which attracts attention from both industry and academics. The huge number of cores means high parallelism and also powerful computation ...

GPU computing offers tremendous potential to accelerate complex scientific applications and is becoming a leading force in speeding up seismic imaging and velocity analysis techniques. Developing portable code is a challenge that can be overcome using ...

Discrete event simulation (DES) has been widely adopted for simulating communication systems such as computer networks. As the network size and complexity of communication patterns increases, the complexity of simulation tools and the execution time of ...

Stream processing or data-flow programming is a compute paradigm that has been around for decades in many forms yet has failed garner the same attention as other mainstream languages and libraries (e.g., C++ or OpenMP [15]). Stream processing has great ...

Java Util Concurrent (JUC) is a widely used library in multithreaded Java applications. JUC provides a variety of tools such as locks, thread pools and blocking queues. Many of these constructs use Thread Park, a mechanism which allows threads to be ...

Nowadays multicore computers are easy to find everywhere, from mobile phones to high end servers. However, producing parallel programs that take advantage of these computers is not easy: parallel programs are error prone and finding these errors and ...

In this work, we analyze efficient communication methods for a grid of many-core processors in the absence of cache coherence. For this study, we build a multi-chip processor with 240 tightly connected cores and demonstrate its scalability. This ...

Although logically available, applications may not exploit enough instantaneous communication concurrency to maximize hardware utilization on HPC systems. This is exacerbated in hybrid programming models such as SPMD+OpenMP. We present the design of a "...

The free lunch of ever increasing clock frequencies is over. Performance-critical sequential software must be parallelized, and this is tedious, hard, buggy, knowledge-intensive, and time-consuming. In order to assist software engineers appropriately, ...

Stream computing is a popular paradigm for parallel and distributed computing, which features computing nodes connected by first-in first-out (FIFO) data channels. To increase the efficiency of communication links and boost application throughput, ...

Computational accelerators, such as manycore NVIDIA GPUs, Intel Xeon Phi and FPGAs, are becoming common in work-stations, servers and supercomputers for scientific and engineering applications. Efficiently exploiting the massive parallelism these ...