high performance computing on graphics processing units: hgpu.org

We show that the stochasticity in training ResNets for image classification on GPUs in TensorFlow is dominated by the non-determinism from GPUs, rather than by the initialisation of the weights and biases of the network or by the sequence of minibatches given. The standard deviation of test set accuracy is 0.02 with fixed seeds, compared […]

Since the recent advance in microprocessor design, the optimization of computing software becomes more and more technical. One of the difficulties is to transform sequential algorithms into parallel ones. A possible solution is the task-based design. In this approach, it is possible to describe the parallelization possibilities of the algorithm automatically. The task-based design is […]

We introduce a multi-platform portable implementation of the NonLocal Means methodology aimed at noise removal from remotely sensed images. It is particularly suited for hyperspectral sensors for which real-time applications are not possible with only CPU based algorithms. In the last decades computational devices have usually been a compound of cross-vendor sets of specifications (heterogeneous […]

Mixed reality (MR) applications are expected to become common when 5G goes mainstream. However, the latency requirements are challenging to meet due to the resources required by video-based remoting of graphics, that is, decoding video codecs. We propose an approach towards tackling this challenge: a client-server implementation for transacting intermediate representation (IR) between a mobile […]

Due to the ever-increasing computational demand of automotive applications, and in particular autonomous driving functionalities, the automotive industry and supply vendors are starting to adopt parallel and heterogeneous embedded platforms for their products. However, C and C++, the currently dominating programming languages in this industry, do not provide sufficient mechanisms to target such platforms. Established […]

Since the last decade, the main components of computer systems have been evolving, diversifying, to overcome their physical limits and to minimize their energy footprint. Hardware specialization and heterogeneity have become key to design more efficient systems and tackle ever-important problems with ever-larger volumes of data. However, to fully take advantage of the new hardware, […]

Sparse linear algebra is central to many scientific programs, yet compilers fail to optimize it well. High-performance libraries are available, but adoption costs are significant. Moreover, libraries tie programs into vendor-specific software and hardware ecosystems, creating non-portable code. In this paper, we develop a new approach based on our specification Language for implementers of Linear […]

Recently, keyword search on Knowledge Graphs (KGs) becomes popular. Typical keyword search approaches aim at finding a concise subgraph from a KG, which can reflect a close relationship among all input keywords. The connection paths between keywords are selected in a way that leads to a result subgraph with a better semantic score. However, such […]

Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms. However, most of the pruning techniques are essentially trade-offs between model accuracy and regularity which lead to impaired inference accuracy and limited on-device acceleration performance. To solve the problem, […]

This paper describes a novel approach for on demand volumetric texture synthesis based on a deep learning framework that allows for the generation of high quality 3D data at interactive rates. Based on a few example images of textures, a generative network is trained to synthesize coherent portions of solid textures of arbitrary sizes that […]

Polyhedral compilers automatically parallelize sequential programs for multi- and many-core architectures, such as CPU and GPU. However, parallel code generated by state-ofthe-art polyhedral compilers often lacks performance portability, because the existing compilers are usually optimized toward only a single particular architecture (e.g., GPU). Moreover, even on their target architecture, polyhedral compilers sometimes fail to reach […]

This work proposes a GPU tensor core approach that encodes the arithmetic reduction of n numbers as a set of chained mxm matrix multiply accumulate (MMA) operations executed in parallel by GPU tensor cores. The asymptotic running time of the proposed chained tensor core approach is T(n)=5 log_m^2 n and its speedup is S=4/5 log_2 […]