high performance computing on graphics processing units: hgpu.org

Stencil computations are often the computeintensive kernel in many scientific applications. With the increasing demand for computational accuracy, and the emergence of massively data-parallel high-bandwidth architectures like GPUs, stencils have steadily become more complex in terms of the stencil order, data accesses, and reuse patterns. Many prior efforts have focused on optimizing simpler stencil computations […]

These days the field of Artificial Intelligence (and its many subfields) is moving really fast, many new techniques are becoming available from various different subfields. However, many of these algorithms are only made to run on very powerful research workstations without considering how they can be used on real-world hardware, be it embedded hardware, powerful […]

The unrivaled computing capabilities of modern GPUs meet the demand of processing massive amounts of data seen in many application domains. While traditional HPC systems support applications as standalone entities that occupy entire GPUs, there are GPU-based DBMSs where multiple tasks are meant to be run at the same time in the same device. To […]

Over the past few years machine learning has seen a renewed explosion of interest, following a number of studies showing the effectiveness of neural networks in a range of tasks which had previously been considered incredibly hard. Neural networks’ effectiveness in the fields of image recognition and natural language processing stems primarily from the vast […]

Digital audio synthesis has become an important component of modern music production with techniques that can produce realistic simulations of real instruments. Physical modelling sound synthesis is a category of audio synthesis that uses mathematical models to emulate the physical phenomena of acoustic musical instruments including drum membranes, air columns and strings. The synthesis of […]

In this paper, we propose and investigate a variety of distributed deep learning strategies for automatic speech recognition (ASR) and evaluate them with a state-of-the-art Long short-term memory (LSTM) acoustic model on the 2000-hour Switchboard (SWB2000), which is one of the most widely used datasets for ASR performance benchmark. We first investigate what are the […]

Over recent years heterogeneous systems have become more prevalent across HPC systems, with over 100 supercomputers in the TOP500 incorporating GPUs or other accelerators. These hardware platforms have different performance characteristics and optimization requirements. In order to make the most of multiple accelerators a developer has to provide implementations of their algorithms tuned for each […]

Traditional optimizing compilers rely on rewrite rules to iteratively apply program transformations. This iterative approach hides optimization opportunities behind intermediate transformation steps. For instance, vectorization can only be applied to the innermost loop in a nest: one must first perform a loop interchange before even considering vectorization of an outer loop. In contrast, we propose […]

Large scale deep neural networks profited from an emerging class of AI accelerators. Although the accelerators are specialized for machine learning, some of their designs are general enough for other computing intensive applications. Cloud TPU, as one of them, offers tremendous computing resources and is easily accessible through TensorFlow by expressing the computation in a […]

In our study, we present the results of the implementation of the SHA-512 algorithm in FPGAs. The distinguished element of our work is that we conducted the work using OpenCL for FPGA, which is a relatively new development method for reconfigurable logic. We examine loop unrolling as an OpenCL performance optimization method and compare the […]

Graphics Processing Units (GPUs) critically rely on a complex system software stack comprising kernel- and userspace drivers and Just-in-time (JIT) compilers. Yet, existing GPU simulators typically abstract away details of the software stack and GPU instruction set. Partly, this is because GPU vendors rarely release sufficient information about their latest GPU products. However, this is […]

Training machine learning (ML) models on large datasets requires considerable computing power. To speed up training, it is typical to distribute training across several machines, often with specialized hardware like GPUs or TPUs. Managing a distributed training job is complex and requires dealing with resource contention, distributed configurations, monitoring, and fault tolerance. In this paper, […]