GPU-Based acceleration of an automatic white matter segmentation algorithm using CUDA

Three-dimensional Light Detection and Ranging (3D LiDAR) sensors are widely used in autonomous vehicles including localization, sensing, and mapping. However, their significant computational requirements remains a major limitation. Although high-end Graphics Processing Units (GPUs) and Central Processing Units (CPUs) can solve the aforementioned problem, the cost and power consumption hinder the widespread adoption of 3-D LiDAR in commercial vehicles. To overcome these limitations, this paper presents a novel high-efficiency 3D mapping method that aims to achieve faster computation speeds than CPUs while providing better energy efficiency compared to GPUs. First, an optimized real-time LiDAR data preprocessing procedure is proposed, which is based on a fixed-point computation framework. Second, to accelerate the LiDAR point cloud matching process, we introduce a new processing architecture called Hardware Accelerated Brute Force Nearest Neighbor (HA-BFNN). Third, we further enhance real-time performance and energy efficiency by proposing a streaming FPGA accelerator architecture for both HA-BFNN and the Iterative Closest Point (ICP) algorithm. Experimental results show that our custom test board, based on the AMD Kintex-7 chip, accelerates point cloud matching, completing single-frame calculations in 5.76 ms, significantly outperforming other 3D mapping implementations on both CPUs and GPUs. Moreover, our FPGA implementation achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$17.36\times $</tex-math> </inline-formula> speedup in execution time compared to CPU implementations. Finally, the proposed system enables real-time performance while consuming only 3.4W of power, maintaining accuracy comparable to software counterparts and even state-of-the-art 3D mapping methods.

Reasonable trade-off between the ASIC performance and GPP flexibility is the main objective of reconfigurable computing systems. Dynamic Reconfigurable computing platform using embedded just-in-time (JIT) compilation is the most flexible platform among others. All complex computing kernels can be translated to bitstream to be executed on FPGA using an embedded processor of dedicated specialized hardware. The main challenge in these systems is FPGA design automation time. Executing the CAD algorithms on embedded processor is too time-consuming and normally is not feasible for real applications. Placement is the most computative part of CAD algorithm. Therefore, a new FPGA placement algorithm is proposed in this paper which makes reasonable trade-off between execution time and quality of placement. The proposed algorithm includes two stages: force-directed placement and simulated annealing placement. The proposed algorithm is very low execution time to be useful in JIT compilation without considerable degradation on the quality of placement. Experimental results show $$2.33\times $$2.33A— speedup in execution time in cost of 3Â % overhead in channel tracks numbers.

In this article, we describe the development and validation of an automatic algorithm to segment brain from extracranial tissues, and to classify intracranial tissues as cerebrospinal fluid (CSF), gray matter (GM), white matter (WM) or pathology. T1 weighted spin echo, dual echo fast spin echo (T2 weighted and proton density (PD) weighted images) and fast Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images were acquired in 100 normal patients and 9 multiple sclerosis (MS) patients. One of the normal studies had synthesized MS-like lesions superimposed. This allowed precise measurement of the accuracy of the classification. The 9 MS patients were imaged twice in one week. The algorithm was applied to these data sets to measure reproducibility. The accuracy was measured based on the synthetic lesion images, where the true voxel class was known. Ninety-six percent of normal intradural tissue voxels (GM, WM, and CSF) were labeled correctly, and 94% of pathological tissues were labeled correctly. A low coefficient of variation (COV) was found (mean, 4.1%) for measurement of brain tissues and pathology when comparing MRI scans on the 9 patients. A totally automatic segmentation algorithm has been described which accurately and reproducibly segments and classifies intradural tissues based on both synthetic and actual images.

GPU acceleration of MPAS microphysics WSM6 using OpenACC directives: Performance and verification

SummaryMany e‐business or social network servers have been constructed on cloud. On such open environments, private data of massive users have to be protected by encrypting, such as using Advanced Encryption Standard (AES), and furthermore, this process must be finished in a short time for users' better experience. This gives huge pressure on cloud servers, especially common servers, such as web servers. We urgently need an inexpensive and highly efficient method to relieve cloud servers' pressure. Fortunately, many cores of a graphics processing unit (GPU) can undertake this hard mission because of stronger computing power and lower price. The GPU environments can be virtualized on demand by cloud through the vCUDA technology. Of course, for those clouds not equipped with a GPU, a central processing unit (CPU) can still work as multithreads in parallel. Thus, in a cloud, AES can be parallelized using many cores of a GPU or multicores of a CPU with high efficiency and low cost. For typical cloud applications, such as web services, there are massive users and each one has short plaintext. If we simply parallelize AES in such an application, we cannot obtain better performance because of the GPU's extra data transferring cost. Thus, we coalesce the massive users' data and cut these data into same‐length slices for improving the performance of parallel AES as much as possible. So we design six parallel AES algorithms using GPU parallelism or CPU parallelism, which differ in parallel scope and whether data are coalesced or cut to slices. Specifically, they are coalescent and sliced GPU (GCS), coalescent and unsliced GPU, uncoalescent GPU, coalescent and sliced CPU, coalescent and unsliced CPU, and uncoalescent CPU. Moreover, we implement them on two representative platforms and evaluate their performance. Through comparing their performance, GCS has the best performance among these algorithms. In a cloud with Nvidia GPUs, GCS is a more powerful algorithm for massive users' data encrypting, relatively. Copyright © 2015 John Wiley &amp; Sons, Ltd.

In this paper we propose an interactive approach for object class segmentation of natural images on touch-screen capable mobile devices. The key research question to which this paper tries to give an answer is: can we effectively correct the errors committed by an automatic or semi-automatic figure-ground segmentation algorithm while also providing real time feedback to the user on a low computational power mobile device? Many research works focused on improving automatic or semi-automatic figure-ground segmentation algorithms, but none tried to take advantage of the existing touch-screen technology integrated in most modern mobile devices to optimize the segmentation results of these algorithms. Our key idea is to use super-pixels as interactive buttons that can be quickly tapped by the user to be added or removed from an initial low quality segmentation mask, with the aim of correcting the segmentation errors and produce a satisfying final result. We performed an extensive analysis of the proposed approach by implementing it both on a desktop computer and a mid-range Android device, even though our method is extremely simple, the results we obtained are comparable with those achieved by other state-of-the-art interactive segmentation algorithms. As such, we believe that the proposed approach can be exploited by most image editing mobile applications to provide a simple but highly effective method for interactive object class segmentation.

Introduction: Wound segmentation is a pivotal process of photography, which is an important and prevalent method for skin wound analysis and documentation in forensic science. However, due to the complexity and diversity of forensic wound images, no automatic segmentation algorithm is capable of judging the boundaries of the wound accurately. Meanwhile, the currently approach using lasso tool, though accurate, is time-consuming and labor-intensive for practical application. Materials and Methods: In this paper, interactive segmentation algorithms are applied to skin wound extraction. This application improves the performance of traditional segmentation methods and reduces the time of manual method. In order to verify the segmentation, a software platform hosting image preprocessing, image segmentation and wound area calculation is developed. This platform integrates the manual method and three interactive segmentation methods, and is completely available for forensic wound measurement based on photography technique. Results: The segmentation accuracy is evaluated by precision, recall and F-measure, which are higher than 0.96, 0.95, and 0.96 respectively. The error of the area calculation is less than 5%, with respect to the golden standard. Discussion: These results demonstrate that interactive segmentation is practicable and precise enough to meet the demands of skin wounds measurement based on photography in forensic medicine.KeywordsInteractive segmentationWound measurementForensic medicinePhotography

An accurate and efficient automatic brain tumor segmentation algorithm is important for clinical practice. In recent years, there has been much interest in automatic segmentation algorithms that use convolutional neural networks. In this paper, we propose a novel hierarchical multi-scale segmentation network (HMNet), which contains a high-resolution branch and parallel multi-resolution branches. The high-resolution branch can keep track of the brain tumor’s spatial details, and the multi-resolution feature exchange and fusion allow the network’s receptive fields to adapt to brain tumors of different shapes and sizes. In particular, to overcome the large computational overhead caused by expensive 3D convolution, we propose a lightweight conditional channel weighting block to reduce GPU memory and improve the efficiency of HMNet. We also propose a lightweight multi-resolution feature fusion (LMRF) module to further reduce model complexity and reduce the redundancy of the feature maps. We run tests on the BraTS 2020 dataset to determine how well the proposed network would work. The dice similarity coefficients of HMNet for ET, WT, and TC are 0.781, 0.901, and 0.823, respectively. Many comparative experiments on the BraTS 2020 dataset and other two datasets show that our proposed HMNet has achieved satisfactory performance compared with the SOTA approaches.

MSA Probs is a parallel algorithm developed to align multiple sequence alignment using a central processing unit (CPU). Whereas the CPU has some limitations, such as the inability to parallelize tasks in the processor (latency-oriented). To overcome these limitations, this paper proposes an improved version of MSA Probs that is compatible with a graphical Processing Unit (GPU). This idea helps in enhancing the performance of our algorithm (MSAprobs). To parallelize the sequential algorithm, Compute Unified Device Architecture (CUDA) or OpenCL is commonly used on GPUs. The NIVIDIA API is used to investigate the GPU's computing power. The results of using CPU only in MSAprobs versus the CPU and GPU are compared using two data sets from the Bali Base and OX Bench. The evaluation of the CPU and GPU is done using Threads 1,2 and 4. The results showed that by combining CPU and GPU, performance is improved and execution time is reduced.

A novel parallel algorithm is presented for generating random scale-free networks using the preferential attachment model. The algorithm, named cuPPA, is custom-designed for “single instruction multiple data (SIMD)” style of parallel processing supported by modern processors such as graphical processing units (GPUs). To the best of our knowledge, our algorithm is the first to exploit GPUs, and also the fastest implementation available today, to generate scale-free networks using the preferential attachment model. A detailed performance study is presented to understand the scalability and runtime characteristics of the cuPPA algorithm. Also another version of the algorithm called cuPPA-Hash tailored for multiple GPUs is presented. On a single GPU, the original cuPPA algorithm delivers the best performance, but is challenging to port to multi-GPU implementation. For multi-GPU implementation, cuPPA-Hash has been used as the parallel algorithm to achieve a perfect linear speedup up to 4 GPUs. In one of the best cases, when executed on an NVidia GeForce 1080 GPU, the original cuPPA generates a scale-free network of two billion edges in less than 3 s. On multi-GPU platforms, cuPPA-Hash generates a scale-free network of 16 billion edges in less than 7 s using a machine consisting of 4 NVidia Tesla P100 GPUs.

In the process of electromagnetic interference sources imaging using parabolic reflection, the existing equipment can only collect the electromagnetic intensity information but it is difficult to directly obtain the phase information. In this paper, we have implemented the combination of the gerchberg-saxton algorithm and the transport-of-intensity equation algorithm for wideband phase retrieval on a NVIDIA's graphics processing unit(GPU) with MATLAB to achieve speedup in computation. The efficiency of GPU based implementation is measured and compared with the algorithm on CPU. The results with GPU show performance enhancement over a factor of 9.07 compared with CPU for an image of size 2048×2048 sampling points.

Network coding has recently been widely applied in various networks for system throughput improvement and/or resilience to network dynamics. However, the computational overhead introduced by the network coding operations is not negligible and has become the cornerstone for real deployment of network coding. In this paper, we exploit the computing power of contemporary Graphic Processing Units (GPUs) to accelerate the network coding operations. We proposed three parallel algorithms that maximize the parallelism of the encoding and decoding processes, i.e., the power of GPUs is fully utilized. This paper also shares our optimization design choices and our workarounds to the challenges encountered in working with GPUs. With our implementation of the algorithms, we are able to achieve up to 12 times of speedup over the highly optimized CPU counterpart, using the NVIDIA GPU and the Computer Unified Device Architecture (CUDA) programming model.

This thesis focuses on finding ways to differentiate the gray matter (GM) and white matter (WM) in magnetic resonance (MR) images of the human spinal cord (SC). The aim of this project is to quantify tissue loss in these compartments to study their implications on the progression of multiple sclerosis (MS). To this end, we propose segmentation algorithms that we evaluated on MR images of healthy volunteers. Segmentation of GM and WM in MR images can be done manually by human experts, but manual segmentation is tedious and prone to intra- and inter-rater variability. Therefore, a deterministic automation of this task is necessary. On axial 2D images acquired with a recently proposed MR sequence, called AMIRA, we experiment with various automatic segmentation algorithms. We first use variational model-based segmentation approaches combined with appearance models and later directly apply supervised deep learning to train segmentation networks. Evaluation of the proposed methods shows accurate and precise results, which are on par with manual segmentations. We test the developed deep learning approach on images of conventional MR sequences in the context of a GM segmentation challenge, resulting in superior performance compared to the other competing methods. To further assess the quality of the AMIRA sequence, we apply an already published GM segmentation algorithm to our data, yielding higher accuracy than the same algorithm achieves on images of conventional MR sequences. On a different topic, but related to segmentation, we develop a high-order slice interpolation method to address the large slice distances of images acquired with the AMIRA protocol at different vertebral levels, enabling us to resample our data to intermediate slice positions. From the methodical point of view, this work provides an introduction to computer vision, a mathematically focused perspective on variational segmentation approaches and supervised deep learning, as well as a brief overview of the underlying project's anatomical and medical background.

The calculation of pairwise correlation coefficient on a dataset, known as the correlation matrix, is often used in data analysis, signal processing, pattern recognition, image processing, and bioinformatics. With the state-of-the-art Graphic Processing Units (GPUs) that consist of massive cores capable to do processing up to several Gflops, the calculation of correlation matrix can be accelerated several times over traditional CPUs. However, due to the rapid growth of the data in the digital era, the correlation matrix calculation becomes computing intensive which needs to be executed on multiple GPUs. As of now, GPUs are common components in data center at many institutions. Their GPU deployment tends towards a GPU cluster which each node is equipped with GPUs. In this paper, we propose a parallel computing based on the hybrid MPI/CUDA programming for fast and efficient Pearson correlation matrix calculation on GPU clusters. At coarse grain parallelism, the correlation matrix is partitioned into tiles which are distributed to execute concurrently on many GPUs using MPI. At fine grain level, the CUDA kernel function on each node performs massively parallel computing on a GPU. To balance load across all GPUs, we adopt the work pool model which there is a master node that manages tasks in the work pool and dynamically assign tasks to worker nodes. The result of the evaluation shows that the proposed work can ensure the load balance across different GPUs and thus gives better execution time than using a simple static data partitioning.

The graphics processing unit (GPU) is extensively used in diverse domains, such as finance, machine learning, and image processing. The GPU can be underutilized as multiple applications may not share the same GPU concurrently owing to a memory oversubscription issue. For example, when applications that require fewer computational resources but a larger GPU memory are running instantaneously, the GPU memory may be insufficient; consequently, the number of GPU applications running simultaneously is restricted, decreasing GPU utilization. Further, it can even stop the execution of applications that are running on the GPU. To this end, we propose FlexGPU, which schedules the kernels of the GPU applications that run on the same GPU according to their features. This framework 1) schedules the kernel at the launching time according to its features to improve GPU utilization and 2) temporarily checkpoints and restores non-dependent content in the GPU memory to/from the host memory, which avoids oversubscription of the GPU when out-of-memory failure occurs and allows more kernels to run concurrently on the GPU. The experimental results show that compared to existing methods, our approach demonstrates a 7 times improvement in performance in terms of execution time and enables a 2.5 times increase in the concurrent execution of applications.