Increasing the robustness of CUDA Fermi GPU-based systems

Nowadays Graphical processing Units (GPUs) have become increasingly popular due to their high computational power and low prices. This makes them particularly suitable for high-performance computing applications, like data elaboration and image processing. In these fields, the capability of properly work even in presence of faults is mandatory. This paper presents an innovative approach, that combines a Software Based Self Test & Diagnosis (SBSTD) methodology with a fault mitigation strategy, to increase the robustness of a CUDA Fermi GPU-based system

An improved fault mitigation strategy for CUDA Fermi GPUs

High computation is a predominant requirement in many applications. In this field, Graphic Processing Units (GPUs) are more and more adopted. Low prices and high parallelism let GPUs be attractive, even in safety critical applications. Nonetheless, new methodologies must be studied and developed to increase the dependability of GPUs. This paper presents an improved fault mitigation strategy against permanent faults for CUDA Fermi GPUs. The proposed approach exploits the reverse engineering of the block scheduling policy in CUDA Fermi GPUs in order to minimize the fault mitigation timing overhead. The graceful performance degradation achieved by the proposed technique outperforms multithreaded CPU implementations and other fault mitigation strategies for CUDA GPU, even in presence of multiple permanent faults

Acceleration of parasitic multistatic radar system using GPGPU

This dissertation details the implementation of PMR [Parasitic Multistatic Radar] signal processing chain in the GPGPU [General Purpose Graphic Processing Units] platform. The primary objective of the project is to accelerate the signal processing chain without compromising the algorithm efficiency and to prove that GPGPUs are a promising platform for parasitic radar signal processing

Using Least Variance for Robust Extraction of Systolic Time Intervals

Systolic time intervals (STI) are clinically used as non-invasive predictor of cardiovascular disease. However, algorithm accuracy generally suffers across subjects and physiological states, requiring parameter tuning for robust STI extraction. To address this challenge, an automated methodology of processing with varying tuning parameters was explored. In this work, two STIs were examined: the R-wave pulse transit time to the PPG foot at the ear (rPTT) and the left ventricular ejection time (LVET). Historic feature detection algorithms were used with a range of tuning parameters over a 60 second interval, with least variance used to select the optimal parameter for robust extraction. These least variance algorithms were quantitatively compared to historic, single parameter algorithms using a positive predictive value metric. In order to decrease the runtime of the algorithms, the least variance algorithms were written such that they could run on a GPU using CUDA. Overall, the least variance algorithms were able to extract the features better than the historic algorithms, without sacrificing runtime. In addition to providing this robust and reliable STI extraction, the least variance algorithms can be adapted to extract features from any period data stream

MASSIVELY PARALLEL ALGORITHMS FOR POINT CLOUD BASED OBJECT RECOGNITION ON HETEROGENEOUS ARCHITECTURE

With the advent of new commodity depth sensors, point cloud data processing plays an increasingly important role in object recognition and perception. However, the computational cost of point cloud data processing is extremely high due to the large data size, high dimensionality, and algorithmic complexity. To address the computational challenges of real-time processing, this work investigates the possibilities of using modern heterogeneous computing platforms and its supporting ecosystem such as massively parallel architecture (MPA), computing cluster, compute unified device architecture (CUDA), and multithreaded programming to accelerate the point cloud based object recognition. The aforementioned computing platforms would not yield high performance unless the specific features are properly utilized. Failing that the result actually produces an inferior performance. To achieve the high-speed performance in image descriptor computing, indexing, and matching in point cloud based object recognition, this work explores both coarse and fine grain level parallelism, identifies the acceptable levels of algorithmic approximation, and analyzes various performance impactors. A set of heterogeneous parallel algorithms are designed and implemented in this work. These algorithms include exact and approximate scalable massively parallel image descriptors for descriptor computing, parallel construction of k-dimensional tree (KD-tree) and the forest of KD-trees for descriptor indexing, parallel approximate nearest neighbor search (ANNS) and buffered ANNS (BANNS) on the KD-tree and the forest of KD-trees for descriptor matching. The results show that the proposed massively parallel algorithms on heterogeneous computing platforms can significantly improve the execution time performance of feature computing, indexing, and matching. Meanwhile, this work demonstrates that the heterogeneous computing architectures, with appropriate architecture specific algorithms design and optimization, have the distinct advantages of improving the performance of multimedia applications

High performance numerical modeling of ultra-short laser pulse propagation based on multithreaded parallel hardware

The focus of this study is development of parallelised version of severely sequential and iterative numerical algorithms based on multi-threaded parallel platform such as a graphics processing unit. This requires design and development of a platform-specific numerical solution that can benefit from the parallel capabilities of the chosen platform. Graphics processing unit was chosen as a parallel platform for design and development of a numerical solution for a specific physical model in non-linear optics. This problem appears in describing ultra-short pulse propagation in bulk transparent media that has recently been subject to several theoretical and numerical studies. The mathematical model describing this phenomenon is a challenging and complex problem and its numerical modeling limited on current modern workstations. Numerical modeling of this problem requires a parallelisation of an essentially serial algorithms and elimination of numerical bottlenecks. The main challenge to overcome is parallelisation of the globally non-local mathematical model. This thesis presents a numerical solution for elimination of numerical bottleneck associated with the non-local nature of the mathematical model. The accuracy and performance of the parallel code is identified by back-to-back testing with a similar serial version

An efficient mixed-precision, hybrid CPU-GPU implementation of a fully implicit particle-in-cell algorithm

Recently, a fully implicit, energy- and charge-conserving particle-in-cell method has been proposed for multi-scale, full-f kinetic simulations [G. Chen, et al., J. Comput. Phys. 230,18 (2011)]. The method employs a Jacobian-free Newton-Krylov (JFNK) solver, capable of using very large timesteps without loss of numerical stability or accuracy. A fundamental feature of the method is the segregation of particle-orbit computations from the field solver, while remaining fully self-consistent. This paper describes a very efficient, mixed-precision hybrid CPU-GPU implementation of the implicit PIC algorithm exploiting this feature. The JFNK solver is kept on the CPU in double precision (DP), while the implicit, charge-conserving, and adaptive particle mover is implemented on a GPU (graphics processing unit) using CUDA in single-precision (SP). Performance-oriented optimizations are introduced with the aid of the roofline model. The implicit particle mover algorithm is shown to achieve up to 400 GOp/s on a Nvidia GeForce GTX580. This corresponds to 25% absolute GPU efficiency against the peak theoretical performance, and is about 300 times faster than an equivalent serial CPU (Intel Xeon X5460) execution. For the test case chosen, the mixed-precision hybrid CPU-GPU solver is shown to over-perform the DP CPU-only serial version by a factor of \sim 100, without apparent loss of robustness or accuracy in a challenging long-timescale ion acoustic wave simulation.Comment: 25 pages, 6 figures, submitted to J. Comput. Phy

ANALYZING GENERAL-PURPOSE COMPUTING PERFORMANCE ON GPU

ABSTRACT Analyzing General-Purpose Computing Performance on GPU Graphic Processing Unit (GPU) has become one of the most important components in modern computer systems. GPUs have evolved from a single -purpose graphic rendering hardware to a powerful processor that is capable of handling many different kinds of computing tasks. However, GPUs don’t perform well on every application, and it takes a lot of design effort to get good performance on a GPU. This thesis aims to investigate the relative performance of a GPU vs. CPU. Design effort is held minimum for both CPU implementations and GPU implementations. Matrix multiplication, Advance Encryption Standard (AES) and 32-bit Cyclic Redundancy Check (CRC32) are implemented on both a CPU and GPU. Input data size is varied to test the performance of the CPU and the GPU. The GPU generally has better performance than the CPU for matrix multiplication and AES because of the applications\u27 good instruction and data parallelism. CRC has very poor parallelism, so the CPU performs better. For very small data inputs, the CPU generally outperformed the GPU because of GPU memory transfer overhead