High-performance Workstations Research Articles

Toward Intraoperative Visual Intelligence: Real-Time Surgical Instrument Segmentation for Enhanced Surgical Monitoring.

Open surgery relies heavily on the surgeon's visual acuity and spatial awareness to track instruments within a dynamic and often cluttered surgical field. This system utilizes a head-mounted depth camera to monitor surgical scenes, providing both image data and depth information. The video captured from this camera is scaled down, compressed using MPEG, and transmitted to a high-performance workstation via the RTSP (Real-Time Streaming Protocol), a reliable protocol designed for real-time media transmission. To segment surgical instruments, we utilize the enhanced U-Net with GridMask (EUGNet) for its proven effectiveness in surgical tool segmentation. For rigorous validation, the system's performance reliability and accuracy are evaluated using prerecorded RGB-D surgical videos. This work demonstrates the potential of this system to improve situational awareness, surgical efficiency, and generate data-driven insights within the operating room. In a simulated surgical environment, the system achieves a high accuracy of 85.5% in identifying and segmenting surgical instruments. Furthermore, the wireless video transmission proves reliable with a latency of 200 ms, suitable for real-time processing. These findings represent a promising step towards the development of assistive technologies with the potential to significantly enhance surgical practice.

A System for Mixed-Reality Holographic Overlays of Real-Time Rendered 3D-Reconstructed Imaging Using a Video Pass-through Head-Mounted Display-A Pathway to Future Navigation in Chest Wall Surgery.

Background: Three-dimensional reconstructions of state-of-the-art high-resolution imaging are progressively being used more for preprocedural assessment in thoracic surgery. It is a promising tool that aims to improve patient-specific treatment planning, for example, for minimally invasive or robotic-assisted lung resections. Increasingly available mixed-reality hardware based on video pass-through technology enables the projection of image data as a hologram onto the patient. We describe the novel method of real-time 3D surgical planning in a mixed-reality setting by presenting three representative cases utilizing volume rendering. Materials: A mixed-reality system was set up using a high-performance workstation running a video pass-through-based head-mounted display. Image data from computer tomography were imported and volume-rendered in real-time to be customized through live editing. The image-based hologram was projected onto the patient, highlighting the regions of interest. Results: Three oncological cases were selected to explore the potentials of the mixed-reality system. Two of them presented large tumor masses in the thoracic cavity, while a third case presented an unclear lesion of the chest wall. We aligned real-time rendered 3D holographic image data onto the patient allowing us to investigate the relationship between anatomical structures and their respective body position. Conclusions: The exploration of holographic overlay has proven to be promising in improving preprocedural surgical planning, particularly for complex oncological tasks in the thoracic surgical field. Further studies on outcome-related surgical planning and navigation should therefore be conducted. Ongoing technological progress of extended reality hardware and intelligent software features will most likely enhance applicability and the range of use in surgical fields within the near future.

The LINUX Operating System: An Introduction

Linux is a Unix-like operating system for Intel 386/486/ Pentium based IBM-PCs and compatibles. The kernel of this operating system was written from scratch by Linus Torvalds and, although copyright by the author, may be freely distributed. A world-wide group of enthusiastic volunteers has collaborated in developing many aspects of Linux on the Internet. Linux can run the powerful set of compilers and programming tools (the 'GNU' corpus) of the Free Software Foundation, and XFree86, a port of the X Window System from MIT. Most capabilities associated with high performance workstations, such as networking, shared file systems, electronic mail, TEX, I*TE.X, etc. are freely available for Linux. It can thus transform cheap IBM-PC compatible machines into Unix workstations with considerable capabilities. The author explains how Linux may be obtained, installed and networked. He also describes some interesting applications for Linux that are freely available. One useful feature of Linux is its ability to coexist with other operating systems. Thus a user who has made an investment in DOS/ MS Windows software, may continue running these applications on his machine and install Linux on a separate partition on his existing hard disk. If needed, files from DOS/ MS Windows partitions can be accessed by Linux. The enormous consumer market for IBM-PC compatible machines continually drives down prices of CPU chips, memory, hard disks, CDROMs etc. Linux can convert such machines into powerful workstations that can be used for teaching, research and software development. For professionals who use Unix based workstations at work, Linux permits virtually identical working environments on their personal home machines. For cost conscious educational institutions (especially in developing nations), Linux can create world-class computing environments from cheap, easily maintained, PC clones. Finally, for university students, especially in science and engineering, Linux provides an essentially cost-free path away from DOS into the world of Unix and X Windows

Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks

AbstractThe rapid and accurate taxonomic identification of fossils is of great significance in paleontology, biostratigraphy, and other fields. However, taxonomic identification is often labor-intensive and tedious, and the requisition of extensive prior knowledge about a taxonomic group also requires long-term training. Moreover, identification results are often inconsistent across researchers and communities. Accordingly, in this study, we used deep learning to support taxonomic identification. We used web crawlers to collect the Fossil Image Dataset (FID) via the Internet, obtaining 415,339 images belonging to 50 fossil clades. Then we trained three powerful convolutional neural networks on a high-performance workstation. The Inception-ResNet-v2 architecture achieved an average accuracy of 0.90 in the test dataset when transfer learning was applied. The clades of microfossils and vertebrate fossils exhibited the highest identification accuracies of 0.95 and 0.90, respectively. In contrast, clades of sponges, bryozoans, and trace fossils with various morphologies or with few samples in the dataset exhibited a performance below 0.80. Visual explanation methods further highlighted the discrepancies among different fossil clades and suggested similarities between the identifications made by machine classifiers and taxonomists. Collecting large paleontological datasets from various sources, such as the literature, digitization of dark data, citizen-science data, and public data from the Internet may further enhance deep learning methods and their adoption. Such developments will also possibly lead to image-based systematic taxonomy to be replaced by machine-aided classification in the future. Pioneering studies can include microfossils and some invertebrate fossils. To contribute to this development, we deployed our model on a server for public access at www.ai-fossil.com.

Stochastic cluster models of wideband high-speed-train channel based on passive measurement in in-service 5G-NR networks

In this paper, a passive channel sounding system specifically designed for the 5G is used to conduct channel measurements on sub-6 GHz frequency bands in time-variant cases on high-speed-train with 250 km/h, and a based-cluster channel model based on measurement data is established. This passive channel sounder, built with dual-channel software-defined radio (SDR) peripherals, high-performance mobile workstation and other ancillaries, can be applied to collection of exact 5G downlink signals with center-frequencies of 2−4 GHz, bandwidth of 100 MHz in in-service 5G new radio (NR) networks. Based on post-processing of downlink signals of different types, multi-dimensional channel power spectra in delay, Doppler frequency and direction of arrival, as well as the estimates of parameters of multipath component (MPC) are obtained. A discussion of the difference between this new 5G-NR model and existing WINNER II model is provided and comments on the model applicability for system performance evaluation.

Automatic identification of fossils and abiotic grains during carbonate microfacies analysis using deep convolutional neural networks

Petrographic analysis based on microfacies identification in thin sections is widely used in sedimentary environment interpretation and paleoecological reconstruction. Fossil recognition from microfacies is an essential procedure for petrographers to complete this task. Distinguishing the morphological and microstructural diversity of skeletal fragments requires extensive prior knowledge of fossil morphotypes in microfacies and long training sessions under the microscope. This requirement engenders certain challenges for sedimentologists and paleontologists, especially novices. However, a machine classifier can help address this challenge. In this study, we collected a microfacies image dataset comprising both public data from 1133 references and our own materials (including a total of 30,815 images of 22 fossil and abiotic grain groups). We employed a high-performance workstation to implement four classic deep convolutional neural networks, which have proven to be highly efficient in computer vision. Our framework uses a transfer learning technique, which reuses the pre-trained parameters that are trained on a larger ImageNet dataset as initialization for the network to achieve high accuracy with low computing costs. We obtained up to 95% of the top one and 99% of the top three test accuracies in the Inception ResNet v2 architecture. The machine classifier exhibited 0.99 precision on minerals such as dolomite and pyrite. Although it had some difficulty on samples having similar morphologies, such as bivalve, brachiopod, and ostracod, it nevertheless obtained 0.88 precision. Our machine learning framework demonstrates high accuracy with reproducibility and bias avoidance that is comparable to those of human classifiers. Its application can thus eliminate much of the tedious, manually intensive efforts by human experts conducting routine identification.

The ecological impact of high-performance computing in astrophysics

The importance of computing in astronomy continues to increase, and so is its impact on the environment. When analyzing data or performing simulations, most researchers raise concerns about the time to reach a solution rather than its impact on the environment. Luckily, a reduced time-to-solution due to faster hardware or optimizations in the software generally also leads to a smaller carbon footprint. This is not the case when the reduced wall-clock time is achieved by overclocking the processor, or when using supercomputers. The increase in the popularity of interpreted scripting languages, and the general availability of high-performance workstations form a considerable threat to the environment. A similar concern can be raised about the trend of running single-core instead of adopting efficient many-core programming paradigms. In astronomy, computing is among the top producers of green-house gasses, surpassing telescope operations. Here I hope to raise the awareness of the environmental impact of running non-optimized code on overpowered computer hardware.

PHAST - A Portable High-Level Modern C++ Programming Library for GPUs and Multi-Cores

A decade after the beginning of the many-core era, multi-core CPU and GPU architectures are everywhere, from mobile devices up to high-performance workstations and servers. To this day, programmers willing to harness their power need to express their code via languages and frameworks that often lack of expressivity and high-level abstractions. These solutions, despite allowing users to reach unprecedented performance, can still be a hampering factor for productivity and portability. In this paper we propose PHAST, a modern C++, STL-like, single-source programming library and approach based on multi-dimensional dynamic containers and multi-layered functors that can be targeted on NVIDIA GPUs and multi-core CPUs. Its main purpose is to let programmers write code once for different architectures at a high level of abstraction, to reach high-performance while allowing fine parameter tuning and not shielding code from low-level target-specific optimizations. To assess the value of our proposal, we consider benchmarks from different application domains, and we evaluate their PHAST implementations against CUDA, OpenCL, Kokkos, and SYCL ones from both performance and productivity points of view. We show that PHAST can significantly reduce code complexity metrics while reaching very good performance.

Multilevel Performance Evaluation of Nvidia Grid VCA Using Iray and V-Ray Rendering Engines in 3DS Max Design

High performance computing is increasingly common in technological industries and there are many different solutions available on the market. Determining which computing solution is most cost-effective can be difficult. This study outlines the performance between a single-user, traditional high-performance workstation and a multi-user, virtualized workstation. Along with this direct performance comparison, the impacts of virtualization on rendering performance, GPUs, and the technological industry is evaluated in this study. Through the repeated rendering of two different Computer-Aided Design (CAD) models under varying test scenarios, a pool of data including render times and image quality is collected and analyzed. Two phenomena are observed and explained. One is a diminishing return in GPU power output that is observed after allocating four or more GPUs to a single rendering task. The second is a noticeable point of image-noise convergence during a render that could potentially be calculated and exploited to make rendering more time-efficient. These discoveries may impact the effectiveness of virtual GPU scalability and make time-consuming rendering more efficient for industry users. The NVIDIA GRID Visual Computing Appliance (VCA) is found to be cost effective for research laboratories that have several users with diverse needs.

Implementing High Performance Workstation Virtualization for Data Processing in a Multi-User Microscopy Facility.

Implementing High Performance Workstation Virtualization for Data Processing in a Multi-User Microscopy Facility.

A Co-Simulation Scalar-Potential Finite Difference Method for the Numerical Analysis of Human Exposure to Magneto-Quasi-Static Fields

A two-step approach for the simulation of the exposure of a human body to magneto-quasi-static fields is presented. The co-simulation scalar-potential finite difference method is a calculation scheme, where a distribution of the magnetic vector potential inside the biological tissues is derived from a free space magnetic source field simulation using a tree-cotree gauging algorithm. With the obtained magnetic vector potential, the electric field strength inside the voxel representation of the biological tissues is calculated solving a high-dimensional discrete Poisson equation on a high-performance workstation with multiple graphics processing units (GPUs). An exposure scenario, including an inductive power transfer system, a car model, and an anatomical human body voxel model, is simulated using this approach, and the results are compared with a monolithic scaled-frequency finite difference time domain simulation. The maximum body-internal electric field strength (voxel average) is determined and compared with basic restrictions recommended by the International Commission on Non-Ionizing Radiation Protection.

Launch of the I13-2 data beamline at the Diamond Light Source synchrotron

Users of the Diamond-Manchester Imaging Branchline I13-2 commonly spend many months analysing the large volumes of tomographic data generated in a single beamtime. This is due to the difficulties inherent in performing complicated, computationally-expensive analyses on large datasets with workstations of limited computing power. To improve productivity, a ‘data beamline’ was launched in January 2016. Users are scheduled for visits to the data beamline in the same way as for regular beamlines, with bookings made via the User Administration System and provision of financial support for travel and subsistence. Two high-performance graphics workstations were acquired, with sufficient RAM to enable simultaneous analysis of several tomographic volumes. Users are given high priority on Diamond’s central computing cluster for the duration of their visit, and if necessary, archived data are restored to a high-performance disk array. Within the first six months of operation, thirteen user visits were made, lasting an average of 4.5 days each. The I13-2 data beamline was the first to be launched at Diamond Light Source and, to the authors’ knowledge, the first to be formalised in this way at any synchrotron.

Numerical dosimetry of CDMA/GSM, DCS/PCS and 3G signal jammers

In this study, numerical dosimetry of a code division multiple access (CDMA)/global system of mobile, digital cellular service (DCS)/personal communications service (PCS) and third generation signal jammer working with three antennas is presented. Three anatomical full human body models of different ages and gender exposed to the signal jammer electromagnetic (EM) waves at 900, 1800 and 2100 MHz, simultaneously, are numerically modelled using a high-resolution finite-difference time-domain gridding; solved by a full-wave three-dimensional EM simulation software on a high-performance workstation machine. The average specific absorption rate (SAR) in the whole human body and the peak spatial SAR averaged over 1 g induced in the head and torso biological tissues were evaluated at different distances from the signal jammer above a ground consisting of concrete and soil layers. The maximum radiated power by the signal jammer at different distances against the human body was calculated for the safe exposure in compliance with the Institute of Electrical and Electronics Engineers (IEEE)/American National Standards Institute (ANSI)/Federal Communications Commission (FCC) standard limits.

FluxModeCalculator: an efficient tool for large-scale flux mode computation.

Elementary flux mode (EFM) analysis is a powerful technique for determining the metabolic capacities and robustness of stoichiometric networks. Recently, several improvements have been made to the algorithm for enumerating the EFMs, making the study of large models possible. However, currently these tools require high performance workstations to perform large-scale EFM computations, thus limiting their applicability. We developed a more time and memory efficient implementation of the algorithm for EFM enumeration in MATLAB, called FluxModeCalculator, which enables large-scale EFM computation on ordinary desktop computers. FluxModeCalculator is open source and freely available under the terms of the GNU General Public License v3.0 at http://www.lumc.nl/jan-bert-van-klinken j.b.van_klinken@lumc.nl Supplementary data are available at Bioinformatics online.

EM-KDE: A locality-aware job scheduling policy with distributed semantic caches

In modern query processing systems, the caching facilities are distributed and scale with the number of servers. To maximize the overall system throughput, the distributed system should balance the query loads among servers and also leverage cached results. In particular, leveraging distributed cached data is becoming more important as many systems are being built by connecting many small heterogeneous machines rather than relying on a few high-performance workstations. Although many query scheduling policies exist such as round-robin and load-monitoring, they are not sophisticated enough to both balance the load and leverage cached results. In this paper, we propose distributed query scheduling policies that take into account the dynamic contents of distributed caching infrastructure and employ statistical prediction methods into query scheduling policy.We employ the kernel density estimation derived from recent queries and the well-known exponential moving average (EMA) in order to predict the query distribution in a multi-dimensional problem space that dynamically changes. Based on the estimated query distribution, the front-end scheduler assigns incoming queries so that query workloads are balanced and cached results are reused. Our experiments show that the proposed query scheduling policy outperforms existing policies in terms of both load balancing and cache hit ratio.

Optimizing agent-based transmission models for infectious diseases.

BackgroundInfectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. However, the increasing hardware complexity requires adapted software designs to achieve the full potential of current high-performance workstations.ResultsWe have found large performance differences with a discrete-time ABM for close-contact disease transmission due to data locality. Sorting the population according to the social contact clusters reduced simulation time by a factor of two. Data locality and model performance can also be improved by storing person attributes separately instead of using person objects. Next, decreasing the number of operations by sorting people by health status before processing disease transmission has also a large impact on model performance. Depending of the clinical attack rate, target population and computer hardware, the introduction of the sort phase decreased the run time from 26 % up to more than 70 %. We have investigated the application of parallel programming techniques and found that the speedup is significant but it drops quickly with the number of cores. We observed that the effect of scheduling and workload chunk size is model specific and can make a large difference.ConclusionsInvestment in performance optimization of ABM simulator code can lead to significant run time reductions. The key steps are straightforward: the data structure for the population and sorting people on health status before effecting disease propagation. We believe these conclusions to be valid for a wide range of infectious disease ABMs. We recommend that future studies evaluate the impact of data management, algorithmic procedures and parallelization on model performance.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0612-2) contains supplementary material, which is available to authorized users.

Micromagnetics on high-performance workstation and mobile computational platforms

The feasibility of using high-performance desktop and embedded mobile computational platforms is presented, including multi-core Intel central processing unit, Nvidia desktop graphics processing units, and Nvidia Jetson TK1 Platform. FastMag finite element method-based micromagnetic simulator is used as a testbed, showing high efficiency on all the platforms. Optimization aspects of improving the performance of the mobile systems are discussed. The high performance, low cost, low power consumption, and rapid performance increase of the embedded mobile systems make them a promising candidate for micromagnetic simulations. Such architectures can be used as standalone systems or can be built as low-power computing clusters.

Numerical Computation of the Combined Specific Absorption Rates Induced in Human Head due to Multiple Independent Sources

The combination of the specific absorption rates induced in human head due to multiple independent electromagnetic field sources is computed numerically. The scenario of this paper considers a human making a mobile call by a GSM-900 handset of internal antenna while working on a Laptop computer with Wi-Fi-enabled dipole-antenna broadcasting at 2.4 GHz. The mobile phone in hand and in close proximity to head at check-position is adopted, whereas, the Laptop computer having different Wi-Fi antenna orientations and positioned at different places, with respect to head, are examined. The results show how the specific absorption rates in head are combined when exposed to both sources, simultaneously. The numerical computation is achieved using a high resolution FDTD gridding; solved by a 3-D full-wave electromagnetic simulation software on a high performance workstation machine with Tesla GPU accelerator.

Hybrid architecture for 3D visualization of ultrasonic data

With the current proliferation of multi-core processors and hardware acceleration, large-data processing methods are increasingly being modified for use in parallel and distributed computing environments. In this paper, we present a hybrid architecture for the visualization and processing of large-scale volumetric data. Various hardware environments and technologies are integrated in this architecture to perform interactive operations on very large volumetric datasets. All of the datasets are stored in a data center with a gigabit network environment. Time-consuming data-processing tasks are allocated to compute nodes connected to the same network, while the visualization and interaction operations are executed on a high-performance graphics workstation. OpenCL and OpenMP are used to implement volume rendering algorithms to accelerate visualization of a hierarchical volume data structure using GPUs and multi-core CPUs. Various out-of-core algorithms are also presented to process the large dataset directly. The experimental results indicate that the proposed hybrid architecture and methods are effective and efficient in processing and visualizing very large-volumetric datasets.

High Resolution Numerical Modelling of In-Vehicle Mobile Calls

The scenario of making a mobile phone call inside a vehicle is numerically modeled using a high resolution FDTD gridding; solved by a 3-D full-wave electromagnetic simulation software on a high performance workstation machine with Tesla GPU accelerator. The simulated mobile call inside a vehicle involved; a high resolution human head with shoulder phantom (VH_Head_Model 9), high resolution realistic hand model (posable right-hand), realistic mobile phone handset CAD-models with different antenna types and a vehicle CAD-model (big-sedan Camaro-brand). The impact of the in-vehicle mobile calls on the electromagnetic interaction of the handset antenna and human is anticipated at different operating frequencies, i.e., GSM-900 and GSM-1800, and different handset positions with respect to head, i.e., cheek and tilt-position. A significant degradation in the handset antenna performance was observed due to the presence of the vehicle body while making a mobile call inside. Nevertheless, no change in the peak spatial-average specific absorption rate in head tissues was detected while making a mobile call inside vehicle, as compared with the specific absorption rate due to the mobile call in free-space.