We consider the distributed memory parallel multiplication of a sparse matrix by a dense matrix (SpMM). The dense matrix is often a collection of dense vectors. Standard implementations will multiply the sparse matrix by multiple dense vectors at the same ...

The matrix multiplication algorithm is a fundamental numerical technique in linear algebra and plays a crucial role in many scientific computing applications. Despite the high performance of mainstream basic linear algebra libraries for large-scale dense ...

Reclaiming memory in non-blocking dynamic data structures in unmanaged languages like C/C++ presents a unique challenge due to the risk of use-after-free errors caused by concurrent accesses. Existing safe memory reclamation (SMR) algorithms fall short of ...

Bayesian networks (BNs) are graphical models representing uncertainty in causal discovery, and have been widely used in medical diagnosis and gene analysis due to their effectiveness and good interpretability. However, mainstream BN structure learning ...

Recent research has shown that collaborative computing of CPUs and GPUs in the same system can effectively accelerate large-scale SGD-based matrix factorization (MF), but it faces the problem of limited scalability due to parameter synchronization in the ...

The high concurrency and high throughput characteristics of graphics processing units (GPUs) have made researchers continue to use it to optimize distributed parallel computing architectures. With the upgrading of processor architecture, GPUs allow ...

The vigorous development of high compute-intensive applications has led to the demand for maximizing the concurrency of multicore processors. The best-effort hardware transactional memory(HTM) is an important technology adopted by vendors to improve the ...

Nested (fork-join) parallelism eases parallel programming by enabling high-level expression of parallelism and leaving the mapping between parallel tasks and hardware to the runtime scheduler. A challenge in dynamic scheduling of nested parallelism is how ...

General sparse matrix-matrix multiplication (SpGEMM) is one of the most important mathematical library routines in a number of applications. In recent years, several efficient SpGEMM algorithms have been proposed, however, most of them are based on the ...

There is a widening gap between GPU and other components (CPU, PCIe bus and communication network) in heterogeneous parallel system. The gap forces us to orchestrate cooperative execution among these components much more carefully than ever before. By ...

Maximal clique enumeration (MCE) is a classic problem in graph theory to identify all complete subgraphs in a graph. In prior MCE work, the Bron-Kerbosch algorithm is one of the most popular solutions, and there are several improved algorithms proposed on ...

We present a scalable high-performance computing implementation of an agent-based economic model using distributed <monospace>+</monospace> shared-memory hybrid parallelization paradigms, capable of simulating 1:1 scale models of large economies like the ...

In a parallel computing scenario, the synchronization overhead, needed to coordinate the execution on the parallel computing nodes, can significantly impair the overall execution performance. Typically, synchronization is achieved by adopting a global ...

Static program analysis has been widely applied along the whole process of the program development for bug detection, code optimization, testing, etc. Although researchers have made significant work in static program analysis, it is still challenging to ...

Many Data-Intensive Scalable Computing (DISC) Systems provide easy-to-use functional APIs, and efficient scheduling and execution strategies allowing users to build concise data-parallel programs. In these systems, data transformations are concealed by ...

Prioritized computation is shown promising performance for a large class of graph algorithms. It prioritizes the execution of some vertices that play important roles in determining convergence. For large-scale distributed graph processing, graph ...

Control and management in smart grids are facing many challenges such as scalability, heterogeneity and technology innovation. This requires a transformation from the traditional centralised paradigm into a distributed one. In this article, a new ...

Existing best-effort requester-wins implementations of transactional memory must resort to non-speculative execution to provide forward progress in the presence of transactions that exceed hardware capacity, experience page faults or suffer high-...

Modern applications generate massive amounts of data that is challenging to process or analyse. Graph algorithms have emerged as a solution for the analysis of such data because they can represent the entities participating in the generation of large-...

Task-based programming models such as OpenMP 5.0 and OmpSs are simple to use and powerful enough to exploit task parallelism of applications over multicore, manycore and heterogeneous systems. However, their software-only runtimes introduce relevant ...