Passive radar parallel processing using General-Purpose computing on Graphics Processing Units

In the paper an implementation of signal processing chain for a passive radar is presented. The passive radar which was developed at the Warsaw University of Technology, uses FM radio and DVB-T television transmitters as "illuminators of opportunity". As the computational load associated with passive radar processing is very high, NVIDIA CUDA technology has been employed for effective implementation using parallel processing. The paper contains the description of the algorithms implementation and the performance results analysis

General Purpose Computing on Graphics Processing Units for Accelerated Deep Learning in Neural Networks

Graphics processing units (GPUs) contain a significant number of cores relative to central processing units (CPUs), allowing them to handle high levels of parallelization in multithreading. A general-purpose GPU (GPGPU) is a GPU that has its threads and memory repurposed on a software level to leverage the multithreading made possible by the GPU’s hardware, and thus is an extremely strong platform for intense computing – there is no hardware difference between GPUs and GPGPUs. Deep learning is one such example of intense computing that is best implemented on a GPGPU, as its hardware structure of a grid of blocks, each containing processing threads, can handle the immense number of necessary calculations in parallel. A convolutional neural network (CNN) created for financial data analysis shows this advantage in the runtime of the training and testing of a neural network

Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration.

General-purpose computing on graphics processing units (GPGPU) is shown to dramatically increase the speed of Monte Carlo simulations of photon migration. In a standard simulation of time-resolved photon migration in a semi-infinite geometry, the proposed methodology executed on a low-cost graphics processing unit (GPU) is a factor 1000 faster than simulation performed on a single standard processor. In addition, we address important technical aspects of GPU-based simulations of photon migration. The technique is expected to become a standard method in Monte Carlo simulations of photon migration

GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance

Astrophysical Supercomputing with GPUs: Critical Decisions for Early Adopters

General purpose computing on graphics processing units (GPGPU) is dramatically changing the landscape of high performance computing in astronomy. In this paper, we identify and investigate several key decision areas, with a goal of simplyfing the early adoption of GPGPU in astronomy. We consider the merits of OpenCL as an open standard in order to reduce risks associated with coding in a native, vendor-specific programming environment, and present a GPU programming philosophy based on using brute force solutions. We assert that effective use of new GPU-based supercomputing facilities will require a change in approach from astronomers. This will likely include improved programming training, an increased need for software development best-practice through the use of profiling and related optimisation tools, and a greater reliance on third-party code libraries. As with any new technology, those willing to take the risks, and make the investment of time and effort to become early adopters of GPGPU in astronomy, stand to reap great benefits.Comment: 13 pages, 5 figures, accepted for publication in PAS

Teaching Parallel Programming Using Java

This paper presents an overview of the "Applied Parallel Computing" course taught to final year Software Engineering undergraduate students in Spring 2014 at NUST, Pakistan. The main objective of the course was to introduce practical parallel programming tools and techniques for shared and distributed memory concurrent systems. A unique aspect of the course was that Java was used as the principle programming language. The course was divided into three sections. The first section covered parallel programming techniques for shared memory systems that include multicore and Symmetric Multi-Processor (SMP) systems. In this section, Java threads was taught as a viable programming API for such systems. The second section was dedicated to parallel programming tools meant for distributed memory systems including clusters and network of computers. We used MPJ Express-a Java MPI library-for conducting programming assignments and lab work for this section. The third and the final section covered advanced topics including the MapReduce programming model using Hadoop and the General Purpose Computing on Graphics Processing Units (GPGPU).Comment: 8 Pages, 6 figures, MPJ Express, MPI Java, Teaching Parallel Programmin

Monte Carlo simulations of two-dimensional electron gasses in gallium nitride high electron mobility transistors via general-purpose computing on graphics processing units

The work in this thesis covers two main topics: successfully porting an Ensemble Monte Carlo (EMC) focused on bulk III-V semiconductors on to the graphics processing unit (GPU) and investigating carrier transport in a two-dimensional electron gas (2DEG) created at an Aluminium Gallium Nitride (AlGaN) and Gallium Nit ride (GaN) heterojunction, specifically the effect of introducing non-equilibrium phonons.The programming language used to be able to run on the GPU, NVIDIA CUDA, is introduced. The concept of highly parallel programming is explored, along with the challenges this poses to an EMC simulating semiconductor materials and devices. The changes made to the bulk EMC algorithm are explained, including architectural, memory strategies and execution optimisations. The performance increase related to each change is given, and it is found that the GPU algorithm has a run time that is approximately 30% of the original EMC algorithm. This is the first example of an EMC simulating electron transport in semiconductors on a GPU.A two-dimensional EMC is created to simulate the behaviour of electrons confined in the 2DEG created at an AlGaN/GaN heterojunction. Results are presented for the electron velocity, momentum and energy relaxation times and mobility, which are compared to experimental results from AlGaN/GaN High Electron Mobility Transistors (HEMTs), and agreement is good. No velocity overshoot is observed, in agreement with experiments.Finally, non-equilibrium phonons are introduced to the 2DEG simulation to study their effect on the electron transport. Non-equilibrium phonons are found to reduce the electron velocity due to diffusive heating. However, due to the confinement of electrons, the phonon distribution is only increased in a small volume of reciprocal space and the effects are shown to be weaker than in bulk. The consideration of electron confinement and a non-equilibrium phonon population has not been seen in the current literature