high performance computing on graphics processing units: hgpu.org

This paper wants to focus on providing a characterization of the runtime performances of state-of-the-art implementations of KGE alghoritms, in terms of memory footprint and execution time. Despite the rapidly growing interest in KGE methods, so far little attention has been devoted to their comparison and evaluation; in particular, previous work mainly focused on performance […]

This paper presents FastSVC, a light-weight cross-domain sing voice conversion (SVC) system, which is able to achieve high conversion performance, with inference speed 4x faster than real time on CPUs. FastSVC uses Conformer based phoneme recognizer to extract singer-agnostic linguistic features from singing signals. A feature-wise linear modulation based generator is used to synthesize waveform […]

Hardware technological advances are struggling to match scientific ambition, and a key question is how we can use the transistors that we already have more effectively. This is especially true for HPC, where the tendency is often to throw computation at a problem whereas codes themselves are commonly bound, at-least to some extent, by other […]

Over recent years, graphics processing units (GPUs) have become popular devices to use in procedures that exhibit data-parallelism. Due to high parallel capability, running procedures on a GPU can result in an execution time speedup ranging from a couple times faster to several orders of magnitude faster, compared to executing serially on a central processing […]

The use of FPGAs in heterogeneous systems are valuable because they can be used to architect custom hardware to accelerate a particular application or domain. However, they are notoriously difficult to program. The development of high level synthesis tools like OpenCL make FPGA development more accessible, but not without its own challenges. The synthesized hardware […]

In recent years, heterogeneous computing has emerged as the vital way to increase computers’ performance and energy efficiency by combining diverse hardware devices, such as Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). The rationale behind this trend is that different parts of an application can be offloaded from the main CPU to […]

AMGCL is a header-only C++ library for the solution of large sparse linear systems with algebraic multigrid. The method may be used as a black-box solver for computational problems in various fields, since it does not require any information about the underlying geometry. AMGCL provides an efficient, flexible, and extensible implementation of several iterative solvers […]

Prior research notes that BERT’s computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissions. While recent work seeks to address these scalability issues at pre-training, these issues are also prominent in fine-tuning especially for long sequence tasks like document classification. Our work thus focuses […]

We present the extension of the Tinker-HP package (Lagardère et al., Chem. Sci., 2018,9, 956-972) to the use of Graphics Processing Unit (GPU) cards to accelerate molecular dynamics simulations using polarizable many-body force fields. The new high-performance module allows for an efficient use of single- and multi-GPU architectures ranging from research laboratories to modern pre-exascale […]

Today’s society is increasingly software-driven and dependent on powerful computer technology. Therefore it is important that advancements in the low-level processor hardware are made available for exploitation by a growing number of programmers of differing skill level. However, as we are approaching the end of Moore’s law, hardware designers are finding new and increasingly complex […]

Heterogeneous systems are present from powerful supercomputers, to mobile devices, including desktop computers, thanks to their excellent performance and energy consumption. The ubiquity of these architectures in both desktop systems and medium-sized service servers allow enough variability to exploit a wide range of problems, such as multimedia workloads, video encoding, image filtering and inference in […]

While large neural networks demonstrate higher performance in various tasks, training large networks is difficult due to limitations on GPU memory size. We propose a novel out-of-core algorithm that enables faster training of extremely large-scale neural networks with sizes larger than allotted GPU memory. Under a given memory budget constraint, our scheduling algorithm locally adapts […]