high performance computing on graphics processing units: hgpu.org

Graphics processor units (GPUs) are prevalent in modern computing systems at all scales. They consume a significant fraction of the energy in these systems. However, vendors do not publish the actual cost of the power/energy overhead of their internal microarchitecture. In this paper, we accurately measure the energy consumption of various instructions found in modern […]

This paper shows how the VerCors verification toolset can be used to prove data race freedom and functional correctness of a parallel radix sort algorithm for GPUs. This is a widely used standard sorting implementation for GPGPU programming frameworks and therefore its correctness is of utmost importance. Additionally, it presents the usefulness of VerCors as […]

Pre-trained models have demonstrated their effectiveness in many downstream natural language processing (NLP) tasks. The availability of multilingual pre-trained models enables zero-shot transfer of NLP tasks from high resource languages to low resource ones. However, recent research in improving pre-trained models focuses heavily on English. While it is possible to train the latest neural architectures […]

In this paper we evaluate the performance of FPGAs for high-order stencil computation using High-Level Synthesis. We show that despite the higher computation intensity and on-chip memory requirement of such stencils compared to first-order ones, our design technique with combined spatial and temporal blocking remains effective. This allows us to reach similar, or even higher, […]

The difficulty of deploying various deep learning (DL) models on diverse DL hardwares has boosted the research and development of DL compilers in the community. Several DL compilers have been proposed from both industry and academia such as Tensorflow XLA and TVM. Similarly, the DL compilers take the DL models described in different DL frameworks […]

This paper presents EASYPAP, an easy-to-use programming environment designed to help students to learn parallel programming. EASYPAP features a wide range of 2D computation kernels that the students are invited to parallelize using Pthreads, OpenMP, OpenCL or MPI. Execution of kernels can be interactively visualized, and powerful monitoring tools allow students to observe both the […]

Linear algebra operations have been widely used in big data analytics and scientific computations. Many works have been done on optimizing linear algebra operations on GPUs with regular-shaped input. However, few works are focusing on fully utilizing GPU resources when the input is not regular-shaped. Current optimizations lack of considering fully utilizing the memory bandwidth […]

Pairwise sequence alignment is one of the most computationally intensive kernels in genomic data analysis, accounting for more than 90% of the runtime for key bioinformatics applications. This method is particularly expensive for third-generation sequences due to the high computational cost of analyzing sequences of length between 1Kb and 1Mb. Given the quadratic overhead of […]

In this paper, we present the StarNEig library for solving dense nonsymmetric standard and generalized eigenvalue problems. The library is built on top of the StarPU runtime system and targets both shared and distributed memory machines. Some components of the library have support for GPU acceleration. The library is currently in an early beta state […]

Central to many complex systems, spatial actors require an awareness of their local environment to enable behaviours such as communication and navigation. Complex system simulations represent this behaviour with Fixed Radius Near Neighbours (FRNN) search. This algorithm allows actors to store data at spatial locations and then query the data structure to find all data […]

In the last decade, there have been tectonic shifts in computer hardware because of reaching the physical limits of the sequential CPU performance. As a consequence, current high-performance computing (HPC) systems integrate a wide variety of compute resources with different capabilities and execution models, ranging from multi-core CPUs to many-core accelerators. While such heterogeneous systems […]

Memristor-based, non-von-Neumann architectures performing tensor operations directly in memory are a promising approach to address the ever-increasing demand for energy-efficient, high-throughput hardware accelerators for Machine Learning (ML) inference. A major challenge for the programmability and exploitation of such Computing-InMemory (CIM) architectures consists in the efficient mapping of tensor operations from high-level ML frameworks to fixed-function […]