high performance computing on graphics processing units: hgpu.org

Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loading data takes a significant portion of model training time. As GPU servers are typically expensive, tricks that can save training time are valuable.Slow training is observed especially on real-world applications where exhaustive […]

Energy optimization is an increasingly important aspect of today’s high-performance computing applications. In particular, dynamic voltage and frequency scaling (DVFS) has become a widely adopted solution to balance performance and energy consumption, and hardware vendors provide management libraries that allow the programmer to change both memory and core frequencies manually to minimize energy consumption while […]

For 35 years, ab initio molecular dynamics (AIMD) has been the method of choice for understanding complex materials and molecules at the atomic scale from first principles. However, most applications of AIMD are limited to systems with thousands of atoms due to the high computational complexity. We report that a machine learning-based molecular simulation protocol […]

The development of automated image registration (IR) methods is a well-known issue within the computer vision (CV) field and it has been largely addressed from multiple viewpoints. IR has been applied to a high number of real-world scenarios ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. In the last two decades, […]

General purpose GPUs are now ubiquitous in high-end supercomputing. All but one (the Japanese Fugaku system, which is based on ARM processors) of the announced (pre-)exascale systems contain vast amounts of GPUs that deliver the majority of the performance of these systems. Thus, GPU programming will be a necessity for application developers using high-end HPC […]

This paper describes AutoParBench, a framework to test OpenMP-based automatic parallelization tools. The core idea of this framework is a common representation, called a "JSON snapshot", that normalizes the output produced by auto-parallelizers. By converting—automatically—this output to the common representation, AutoParBench lets us compare auto-parallelizers among themselves, and compare them semantically against a reference collection. […]

Writing efficient programs for heterogeneous platforms is challenging: programmers must deal with multiple programming models, partition work for CPUs and accelerators with different compute capabilities, requiring different amounts of parallelism, and manage memory in multiple distinct address spaces. Consequently, programming models which only require expressing parallelism and data dependences can not only unburden the programmer […]

The use of reduced precision to improve performance metrics such as computation latency and power consumption is a common practice in the embedded systems field. This practice is emerging as a new trend in High Performance Computing (HPC), especially when new error-tolerant applications are considered. However, standard compiler frameworks do not support automated precision customization, […]

We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation with ab initio accuracy. Our tests show that the GPU version is 7 times faster than the CPU version with the same power consumption. The code can scale […]

An install-less, header-only library which is a loosely-coupled collection of utility functions and classes for writing device-side CUDA code (kernels and non-kernel functions). These let us: * Write templated device-side without constantly coming up against not-trivially-templatable bits. * Use standard-library(-like) containers in device-side code (but not have to use them). * Not repeat ourselves as […]

Recent years have seen an increase of heterogeneous architectures combining multi-core CPUs with accelerators such as GPU, FPGA, and Intel Xeon Phi. GPU can achieve significant performance for certain categories of application. Nevertheless, achieving this performance with low-level APIs (e.g. CUDA, OpenCL) requires to rewrite the sequential code, to have a good knowledge of GPU […]

Convolution operations are essential constituents of convolutional neural networks. Their efficient and performance-portable implementation demands tremendous programming effort and fine-tuning. Winograd’s minimal filtering algorithm is a well-known method to reduce the computational complexity of convolution operations. Unfortunately, existing implementations of this algorithm are either vendor-specific or hard-coded to support a small subset of convolutions, thus […]