high performance computing on graphics processing units: hgpu.org

The proliferation of artificial intelligence applications has underscored the need for increased portability among graphic processing units (GPUs) from different vendors. With CUDA as one of the most popular GPU programming languages, CuPBoP (CUDA for Parallelized and Broad-range Processors) aims to provide NVIDIA’s proprietary CUDA language support to a variety of GPU and CPU platforms […]

Sparse matrix multiplication is an important kernel for large-scale graph processing and other data-intensive applications. In this paper, we implement various asynchronous, RDMA-based sparse times dense (SpMM) and sparse times sparse (SpGEMM) algorithms, evaluating their performance running in a distributed memory setting on GPUs. Our RDMA-based implementations use the NVSHMEM communication library for direct, asynchronous […]

All major weather and climate applications are currently developed using languages such as Fortran or C++. This is typical in the domain of high performance computing (HPC), where efficient execution is an important concern. Unfortunately, this approach leads to implementations that intermix optimizations for specific hardware architectures with the high-level numerical methods that are typical […]

A wide range of bioinformatics applications have to deal with a continuously growing amount of data generated by high-throughput sequencing techniques. Exclusively CPU-based workstations fail to keep up with the task. Instead of employing dozens of CPU cluster nodes to increase the computational power, massively parallel accelerators like modern CUDA-enabled GPUs can be used to […]

High performance computing is a topic that has risen to the top in the era of digitalization, AI and automation. Therefore, the search for more cost and time effective ways to implement HPC work is always a subject extensively researched. One part of this is to have hardware that is capable to improve on these […]

In recent years, deep neural network based medical image analysis has become quite powerful and achieved similar results performance-wise as experts. Consequently, the integration of these tools into the clinical routine as clinical decision support systems is highly desired. The benefits of automatic image analysis for clinicians are massive, ranging from improved diagnostic as well […]

Highly parallelized workloads like machine learning training, inferences and general HPC tasks are greatly accelerated using GPU devices. In a cloud computing cluster, serving a GPU’s computation power through multi-tasks sharing is highly demanded since there are always more task requests than the number of GPU available. Existing GPU sharing solutions focus on reducing task-level […]

In this study, we present a novel dataset for training machine learning models translating between OpenMP Fortran and C++ code. To ensure reliability and applicability, the dataset is created from a range of representative open-source OpenMP benchmarks. It is also refined using a meticulous code similarity test. The effectiveness of our dataset is assessed using […]

An auto-tuning framework for GPU devices is presented for tuning application kernels of OpenCL. The GPU tuner employs multi-objective optimization methodology to improve the performance and power consumption of applications. It efficiently explores a user defined solution space comprising of possible tunable algorithmic and hardware counter variations through code transformations. The methodology targets GPU code […]

Addressing performance portability across diverse accelerator architectures has emerged as a major challenge in the development of application and programming systems for high-performance computing environments. Although recent programming systems that focus on performance portability have significantly improved productivity in an effort to meet this challenge, the problem becomes notably more complex when compute nodes are […]

Increasing heterogeneity among HPC platforms requires applications to be frequently ported and tuned, adding burden to developers. Fast evolution of hardware mandates adaptation of algorithms and data structures to get higher performance, while application complexity constantly grows accordingly. Ensuring portability while preserving high performance at large scale along with minimal changes to an already existing […]

OpenCL provides a consistent programming model across CPUs, GPUs, and FPGAs. However, to get reasonable performance out of FPGAs, OpenCL programs created for other platforms need to be modified. These modifications are often vendor-specific, limiting the portability of OpenCL programs between devices from different vendors. In this paper, we propose AFOCL: a cross-vendor portable programming […]