In this paper, we design and evaluate a routine for the efficient generation of block-Jacobi preconditioners on graphics processing units (GPUs). Concretely, to exploit the architecture of the graphics accelerator, we develop a batched Gauss-Jordan ...

Recent scheduling heuristics for task-based applications have managed to improve their by taking into account memory-related properties such as data locality and cache sharing. However, there is still a general lack of tools that can provide insights ...

This work establishes a scalable, easy to use and efficient approach for exploiting SIMD capabilities of modern CPUs, without the need for extensive knowledge of architecture specific instruction sets. We provide a description of a new API, known as UME:...

Many computing systems today are heterogeneous in that they consist of a mix of different types of processing units (e.g., CPUs, GPUs). Each of these processing units has different execution capabilities and energy consumption characteristics. Job ...

Asymmetric multicore processors (AMPs), as those present in ARM big.LITTLE technology, have been proposed as a means to address the end of Dennard power scaling law. The idea of these architectures is to activate only the type (and number) of cores that ...

Detecting strongly connected components (SCC) has been broadly used in many real-world applications. To speedup SCC detection for large-scale graphs, parallel algorithms have been proposed to leverage modern GPUs. Existing GPU implementations are able ...

In this paper, we advocate a composable approach to programming systems with Graphics Processing Units (GPU): programs are developed as compositions of generic, reusable patterns. Current GPU programming approaches either rely on low-level, monolithic ...

The proliferation of accelerators in modern clusters makes efficient coprocessor programming a key requirement if application codes are to achieve high levels of performance with acceptable energy consumption on such platforms. This has led to ...

Traditional widely used parallel programming models and methods focus on data distribution and are suitable for implementing data parallelism. They lack the abstraction of task parallelism and make it inconvenient to separate the applications' high ...