high performance computing on graphics processing units: hgpu.org

Communication among devices in multi-GPU systems plays an important role in terms of performance and scalability. In order to optimize an application, programmers need to know the type and amount of the communication happening among GPUs. Although there are prior works to gather this information in MPI applications on distributed systems and multi-threaded applications on […]

Simulating all threads in a scaled GPU workload results in prohibitive simulation cost. Cycle-level simulation is orders of magnitude slower than native silicon, the only solution is to reduce the amount of work simulated while accurately representing the program. Existing solutions to simulate GPU programs either scale the input size, simulate the first several billion […]

Python has become a dominant programming language for emerging areas like Machine Learning (ML), Deep Learning (DL), and Data Science (DS). An attractive feature of Python is that it provides easy-to-use programming interface while allowing library developers to enhance performance of their applications by harnessing the computing power offered by High Performance Computing (HPC) platforms. […]

This paper describes the application of the code generated by the CAMPARY software to accelerate the solving of linear systems in the least squares sense on Graphics Processing Units (GPUs), in double double, quad double, and octo double precision. The goal is to use accelerators to offset the cost overhead caused by multiple double precision […]

Computationally demanding tasks are typically calculated in dedicated data centers, and real-time visualizations also follow this trend. Some rendering tasks, however, require the highest level of confidentiality so that no other party, besides the owner, can read or see the sensitive data. Here we present a direct volume rendering approach that performs volume rendering directly […]

With the end of Dennard Scaling and the imminent end of Moore’s Law, the search for new ways to improve performance in computing systems is increasing. Nowadays, the main approach is to use hardware accelerations to offload the application. However, while this is a power-efficient approach, their development process is costly and time-consuming. In this […]

AutoDock VINA is one of the most-used docking tools in the early stage of modern drug discovery. It uses a Monte-Carlo based iterated search method and multithreading parallelism scheme on multicore machines to improve docking accuracy and speed. However, virtual screening from huge compound databases is common for modern drug discovery, which puts forward a […]

This review paper systematically summarizes the existing literature on applying classical AI techniques and advanced deep learning algorithms to electric machine drives. It is anticipated that with the rapid progress in deep learning models and embedded hardware platforms, AI-based data-driven approaches will become increasingly popular for the automated high-performance control of electric machines. Additionally, this […]

Enterprises and labs performing computationally expensive data science applications sooner or later face the problem of scale but unconnected infrastructure. For this up-scaling process, an IT service provider can be hired or in-house personnel can attempt to implement a software stack. The first option can be quite expensive if it is just about connecting several […]

Leveraging parallel hardware (e.g. GPUs) to conduct deep neural network (DNN) training/inference, though significantly speeds up the computations, raises several data privacy concerns. Trusted execution environments (TEEs) have emerged as a promising solution to enable privacy-preserving inference and training. TEEs, however, have limited memory and computation resources which renders it not comparable to untrusted parallel […]

Reinforcement Learning (RL) has achieved significant success in application domains such as robotics, games, health care and others. However, training RL agents is very time consuming. Current implementations exhibit poor performance due to challenges such as irregular memory accesses and synchronization overheads. In this work, we propose a framework for generating scalable reinforcement learning implementations […]

GCN (Graph Convolutional Network) has become a promising solution for many applications, such as recommendation systems, social data mining, etc. Many of these applications requires low latency GCN inference. In this paper, we provide a case study of a GCN inference acceleration on FPGA. We explore high-level synthesis programming model to achieve low-latency inference. First, […]