high performance computing on graphics processing units: hgpu.org

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 […]

GPUs are a key enabler of the revolution in machine learning and high performance computing, functioning as de facto co-processors to accelerate large-scale computation. As the programming stack and tool support have matured, GPUs have also become accessible to programmers, who may lack detailed knowledge of the underlying architecture and fail to fully leverage the […]

FPGAs have found increasing adoption in data center applications since a new generation of high-level tools have become available which noticeably reduce development time for FPGA accelerators and still provide high quality of results. There is however no high-level benchmark suite available which specifically enables a comparison of FPGA architectures, programming tools and libraries for […]

The Cpp-Taskflow project addresses the long-standing question: How can we make it easier for developers to write parallel and heterogeneous programs with high performance and simultaneous high productivity? Cpp-Taskflow develops a simple and powerful task programming model to enable efficient implementations of heterogeneous decomposition strategies. Our programming model empowers users with both static and dynamic […]

Object detection is a technology that deals with recognizing classes of objects and their location. It is used in many different areas, such as in face-detecting systems [16, 34, 37], surveillance tools [9], human-machine interfaces [17], and self-driving cars [18, 23, 25, 26, 30]. These days, deep learning object detection approaches have achieved significantly better […]

High-performance computing researchers are trying to find new options, tools to satisfy the performance criteria of a hardware design. FPGA (Field Programmable Gate Array) is one of the accelerators which is widely used for power-efficient applications due to its reconfigurability and high performance. Traditionally FPGA can be programmed using Hardware Description Language (HDL). Using HDL, […]

Tensor computation plays a paramount role in a broad range of domains, including machine learning, data analytics, and scientific computing. The wide adoption of tensor computation and its huge computation cost has led to high demand for flexible, portable, and high-performance library implementation on heterogeneous hardware accelerators such as GPUs and FPGAs. However, the current […]

Deep Learning (DL) model-based AI services are increasingly offered in a variety of predictive analytics services such as computer vision, natural language processing, speech recognition. However, the quality of the DL models can degrade over time due to changes in the input data distribution, thereby requiring periodic model updates. Although cloud data-centers can meet the […]

GPUs are playing an increasingly important role in general-purpose computing. Many algorithms require synchronizations at different levels of granularity in a single GPU. Additionally, the emergence of dense GPU nodes also calls for multi-GPU synchronization. Nvidia’s latest CUDA provides a variety of synchronization methods. Until now, there is no full understanding of the characteristics of […]

Deploying deep learning models on mobile devices draws more and more attention recently. However, designing an efficient inference engine on devices is under the great challenges of model compatibility, device diversity, and resource limitation. To deal with these challenges, we propose Mobile Neural Network (MNN), a universal and efficient inference engine tailored to mobile applications. […]

Face recognition applications are widely used today for a variety of tasks, whether personal or professional. When looking for a service that provides face detection and classification, it is easy to find several solutions. In this project another way is described so that it is possible to perform this task according to the desired needs […]