Multi-core Research Articles

Lavender: An Efficient Resource Partitioning Framework for Large-Scale Job Colocation

Workload consolidation is a widely used approach to enhance resource utilization in modern data centers. However, the concurrent execution of multiple jobs on a shared server introduces contention for essential shared resources such as CPU cores, Last Level Cache, and memory bandwidth. This contention negatively impacts job performance, leading to significant degradation in throughput. To mitigate resource contention, effective resource isolation techniques at the software or hardware level can be employed to partition the shared resources among colocated jobs. However, existing solutions for resource partitioning often assume a limited number of jobs that can be colocated, making them unsuitable for scenarios with a large-scale job colocation due to several critical challenges. In this study, we propose Lavender, a framework specifically designed for addressing large-scale resource partitioning problems. Lavender incorporates several key techniques to tackle the challenges associated with large-scale resource partitioning, ensuring efficiency, adaptivity, and optimality. We conducted comprehensive evaluations of Lavender to validate its performance and analyze the reasons for its advantages. The experimental results demonstrate that Lavender significantly outperforms state-of-the-art baselines. Lavender is publicly available at https://github.com/yanxiaoqi932/OpenSourceLavender.

The Digital Media Art and Design System under the Integration of Computer Big Data ChatGPT Technology

Traditional digital media art and design systems take a long time to run and cannot handle a large number of objects simultaneously. Therefore, the author proposes a digital media art and design method based on ChatGPT technology. ChatGPT technology is an important part of networking.Prepare to provide conference support for various applications such as customer service, personal assistant, robotic interview, and so on. ChatGPT can not only answer the question that is set, but also solve the question openly.The comprehensiveness and intelligence of ChatGPT have attracted widespread attention since its launch. The hardware system adopts dual core TMS320C6657 and DDR3Thel1333 series processors as the core circuit, together with high speed external storage, to expand the image transmission line of multi-data; System software uses module architecture to design image modules, video modules, and voice modules, all of which are integrated into the core of the system as modules. In this experiment, 10 images were transferred and the running time was tested. Experimental results showed that, the traditional digital media art and design system had three errors, which took more time to run.

GPU-based key-frame selection of pulmonary ultrasound images to detect COVID-19

In the last decades, technological advances have led to a considerable increase in computing power constraints to simulate complex phenomena in various application fields, among which are climate, physics, genomics and medical diagnosis. Often, accurate results in real time, or quasi real time, are needed, especially if related to a process requiring rapid interventions. To deal with such demands, more sophisticated approaches have been designed, including GPUs, multicore processors and hardware accelerators. Supercomputers manage high amounts of data at a very high speed; however, despite their considerable performance, their limitations are due to maintenance costs, rapid obsolescence and notable energy consumption. New processing architectures and GPUs in the medical field can provide diagnostic and therapeutic support whenever the patient is subject to risk. In this context, image processing as an aid to diagnosis, in particular pulmonary ultrasound to detect COVID-19, represents a promising diagnostic tool with the ability to discriminate between different degrees of disease. This technique has several advantages, such as no radiation exposure, low costs, the availability of follow-up tests and the ease of use even with limited resources. This work aims to identify the best approach to optimize and parallelize the selection of the most significant frames of a video which is given as the input to the classification network that will differentiate between healthy and COVID patients. Three approaches have been evaluated: histogram, entropy and ResNet-50, followed by a K-means clustering. Results highlight the third approach as the most accurate, simultaneously showing GPUs significantly lowering all processing times.

Self-centering multiple rocking core systems for mitigating higher mode effects

The structural member forces of mid-story and high-rise self-centering energy-absorbing dual rocking core (SEDRC) systems can be significantly aggravated by higher mode effects. This study aims to introduce the multiple rocking mechanisms into the SEDRC system and propose the self-centering multiple rocking core system (denoted as SMRC system) to reduce the structural member force demands resulting from the higher mode effects. The working principle of the SMRC system is discussed. The performance-based design (PBD) procedures for the SMRC system are presented. The comparative dynamic history analyses are conducted on a 9-story SMRC and two 9-story SEDRC systems. Incremental dynamic analyses (IDAs) are carried out to investigate the dynamic behaviors of the considered systems under increasing seismic intensities. The accuracy of the developed designed procedures is verified with the evidence that the developed SMRC system can achieve the design displacement under design earthquakes. The multiple rocking mechanism can efficiently mitigate structural member force demands caused by the effects of higher mode. The SMRC system can achieve better seismic performance with lower structural member force demands than the SEDRC systems when the earthquake intensity is not higher than the maximum considered earthquakes (MCE). Nevertheless, the SMRC system shows worse performance than the SEDRC systems in resisting structural collapse when the intensity is higher than MCE, which could be a research gap for further investigations.

Temperature self-compensated fiber-optic Pb(II) sensor with improved selectivity by Resorcylaldehyde post-modified organic metal framework

Post-modification of metal-organic framework (MOF) materials is an effective means to enhance their efficient and selective adsorption for heavy metal ions. In this article, we presented a reflection-type optical fiber surface plasmon resonance (SPR) sensor based on dual core fiber (DCF) coated with UiO-66-RSA (UiO-66-NH2 post-modified with resorcinol aldehyde) film for the specific detection of Pb(II) in aqueous media. The composite material UiO-66-RSA exhibits stronger affinity and specificity for Pb(II) than UiO-66-NH2. The adsorption of Pb(II) can change the equivalent refractive index (RI) of UiO-66-RSA film, causing a regular shift in SPR spectra. Experiments have shown that the limit of detection (LOD) of the SPR Pb(II) sensor can reach 0.1 nM. Meanwhile, research has found that the SPR sensor is accompanied by temperature crosstalk of 0.356 nm/°C. Therefore, we constructed a Michelson temperature interferometer using polymer micro tips on the end face of the fiber core to achieve temperature compensation. Based on the above two sensing channels, dual parameter demodulation of Pb(II) concentration and temperature can be achieved with an error rate of less than 0.5 %. This article has potential research value in exploring the application of post modification techniques of MOF in fiber optic lead ion detection.

VSP wavefields separating method based on parallel local slope scanning

Due to the detectors being distributed in the target medium, vertical seismic profile (VSP) is a seismic observation method that has the advantages of high resolution and a high signal-to-noise ratio. Separating the mixed wavefields into upgoing and downgoing waves can obtain more obvious dynamic and kinematic characteristics of seismic waves, guiding subsequent imaging and interpretation. The traditional method is mainly based on the pickup of first breaks. Flattening the global seismic data by first breaks can enhance the seismic events through techniques like median filter and singular value decomposition (SVD). However, this method relies on high-precision pickup of first breaks and is limited by zero offset. To address this limitation, we introduce an improved median filtering separation method. This method employs separations of the local dip angles into positive and negative through multi-window scanning (MWS). Due to the high accuracy and robustness of this method in 2-D VSP data, we propose to use the local dip angle of the wavefields to median filter the wavefield through positive and negative angles to obtain upgoing and downgoing waves. This method for wavefield separation is optimized by iteratively identifying the directions of the seismic data. However, this optimization comes at the cost of increased computational requirements, especially under high-precision conditions. To help alleviate this problem, we use multi-thread parallel computing technique on a multi-core central processing unit (CPU) to improve computational efficiency. Finally, we validate the proposed method by testing it on synthetic seismic data and field VSP data, respectively. The results show that this wavefield separation method has advantages in terms of accuracy and robustness compared to the median filtering method based on the first break picking.

Performance Study of Object Tracking with Multiple Kalman Filters in Autonomous Driving Systems

Object tracking is an important and central aspect of autonomous driving, as it underlies the obstacle detection and avoidance systems of any type of autonomous vehicles. A widely used method for tracking is based on Kalman filters, both for linear and non-linear cases, with different computational burden. Unfortunately, object tracking algorithms are computationally intensive, and they may not easily meet the efficiency and responsiveness requirements of real-time applications such as autonomous driving. This issue motivates ad-hoc investigations to speed up the computation and make Kalman filtering available even within limited computational power. This paper carry out a performance evaluation of a Kalman filter based object tracking system taken from a real tramway use-case, and aims at improving its performance efficiency by leveraging parallelization. In particular, this work analyzes the possibilities of execution parallelization on multi-core processors, proposing a target-specific optimization approach and comparing the obtained results, then summing them in general lessons learned. Our technique achieves up to 80% reduction of single frame processing time in the most crowded cases.

Comprehensive framework of GPU-accelerated image reconstruction for photoacoustic computed tomography.

Photoacoustic computed tomography (PACT) is a promising non-invasive imaging technique for both life science and clinical implementations. To achieve fast imaging speed, modern PACT systems have equipped arrays that have hundreds to thousands of ultrasound transducer (UST) elements, and the element number continues to increase. However, large number of UST elements with parallel data acquisition could generate a massive data size, making it very challenging to realize fast image reconstruction. Although several research groups have developed GPU-accelerated method for PACT, there lacks an explicit and feasible step-by-step description of GPU-based algorithms for various hardware platforms. In this study, we propose a comprehensive framework for developing GPU-accelerated PACT image reconstruction (GPU-accelerated photoacoustic computed tomography), to help the research community to grasp this advanced image reconstruction method. We leverage widely accessible open-source parallel computing tools, including Python multiprocessing-based parallelism, Taichi Lang for Python, CUDA, and possible other backends. We demonstrate that our framework promotes significant performance of PACT reconstruction, enabling faster analysis and real-time applications. Besides, we also described how to realize parallel computing on various hardware configurations, including multicore CPU, single GPU, and multiple GPUs platform. Notably, our framework can achieve an effective rate of times when reconstructing extremely large-scale three-dimensional PACT images on a dual-GPU platform compared to a 24-core workstation CPU. In this paper, we share example codes via GitHub. Our approach allows for easy adoption and adaptation by the research community, fostering implementations of PACT for both life science and medicine.

FinaleToolkit: Accelerating Cell-Free DNA Fragmentation Analysis with a High-Speed Computational Toolkit.

Cell-free DNA (cfDNA) fragmentation pattern represents a promising non-invasive biomarker for disease diagnosis and prognosis. Numerous fragmentation features, such as end motif and window protection score (WPS), have been characterized in cfDNA genomic sequencing. However, the analytical tools developed in these studies are often not released to the liquid biopsy community or are inefficient for genome-wide analysis in large datasets. To address this gap, we have developed FinaleToolkit, a fast and memory efficient Python package designed to generate comprehensive fragmentation features from large cfDNA genomic sequencing data. For instance, FinaleToolkit can generate genome-wide WPS features from a ~100X cfDNA whole-genome sequencing (WGS) dataset in 1.2 hours using 16 CPU cores, offering up to a ~50-fold increase in processing speed compared to original implementations in the same dataset. We have benchmarked FinaleToolkit against original studies or implementations where possible, confirming its efficacy. Furthermore, FinaleToolkit enabled the genome-wide analysis of fragmentation patterns over arbitrary genomic intervals, significantly boosting the performance for cancer early detection. FinaleToolkit is open source and thoroughly documented with both command line interface and Python application programming interface (API) to facilitate its widespread adoption and use within the research community: https://github.com/epifluidlab/FinaleToolkit.

GPUPRSI: GPU implementation of seismic interferometry for retrieving reflection responses from passive source seismic recordings

Passive seismic exploration is an environmentally friendly, economical, and highly accessible exploration method that is widely used in different-scale subsurface imaging. Retrieving reflections from ambient noise is a newly developed passive seismic method that can be used in many fields such as mineral exploration and near-surface imaging, but the interferometry calculation is time-consuming because it requires a longer period of data acquisition to improve the signal-to-noise ratio of the retrieved reflections. In this study, we introduced a graphical processing unit (GPU)- based implementation of seismic interferometry for retrieving reflection responses from passive source seismic recordings. Because all traces are involved in the computation process, but the size of passive source data often exceeds available memory, repeated disk reads lead to a decrease in computational efficiency. We design a strategy of grouping computations followed by stacking to minimize disk input and output, simultaneously keeping the memory requirements low. Passive source data is read and written only once, and there is no requirement for the memory size to be greater than the data size. Additionally, acceleration technologies such as asynchronous execution, asynchronous memory transfer, and GPU-accelerated libraries are used. We test the efficiency using short data where the number of sampling points per trace is on the order of 30,000, and long data, where the number of sampling points per trace is on the order of 30,000,000. For short data, the average speedup is 4 compared with a multi-core central processing unit (CPU); for long data, the speedup can reach 24. The GPU-based implementation of interferometry greatly reduces the calculation time for retrieving reflections from passive source seismic recordings, providing a solution to the problem of large calculation in three-dimensional (3D) passive source reflection exploration.

Single layer dual hollow core antiresonant fiber based polarization beam splitters

To confront the growing challenges of integrated photonics, optical devices need to be compact in size to integrate them in communication networks where a polarization beam splitter device may play a crucial role. In this regard, hollow core antiresonant fibers have garnered significant attention as a versatile platform, for achieving efficient polarization splitting, owing to their promising characteristics. In this article, traditional and available multilayer complex cladding geometry, in dual hollow core antiresonant fiber, is simplified to single layer arrangement and created efficient polarization beam splitters device. First, we introduced a splitter with single layer cladding geometry that simultaneously presents a short device length of 2.24 cm and an ultra-wide bandwidth of 415 nm, with an extinction ratio exceeding 20 dB. This bandwidth also encompasses widely used communication wavelengths of 1.31μm and 1.55μm. Moreover, a careful investigation of geometrical dimensions, nesting, and conjoined tubes inclusion suggests that the device structure having two horizontal nested elements has an excellent splitting performance with a concise splitter length of 2.06 cm and an ultra-wide bandwidth of 465 nm, covering most telecom bands. Notably, the splitters have the highest extinction ratio of around 140 dB at 1.55μm, along with an effective single mode performance. Therefore, the claimed single layer polarization splitters might be an attractive candidate for the integration of optical devices in communication systems.

High Figure–of–Merit Multi–Core Fiber With Standard Cladding Diameter for Long–Haul and Wide–Band Transmission

High Figure–of–Merit Multi–Core Fiber With Standard Cladding Diameter for Long–Haul and Wide–Band Transmission

UBlade: Efficient Batch Processing for Uncertainty Graph Queries

The study of uncertain graphs is crucial in diverse fields, including but not limited to protein interaction analysis, viral marketing, and network reliability. Processing queries on uncertain graphs presents formidable challenges due to the vast probabilistic space they encapsulate. While existing systems employ batch processing to address these challenges, their performance is often compromised by the suboptimal selection of parallel graph traversal methods, the excessive costs in random number generation, and additional workloads intrinsic to batch processing. In this paper, we introduce uBlade, an efficient batch-processing framework for uncertain graph queries on multi-core CPUs. uBlade utilizes the work-efficient graph traversal, achieving superior parallelism in the batch processing model. Additionally, our Quasi-Sampling technique reduces the random number generation cost by a factor of B, with O(B) denoting the batch size. We further examine the extra workload resulting from batch processing and introduce an efficient strategy to reorder possible worlds, minimizing this associated overhead. Through comprehensive evaluations, we showcase that uBlade achieves up to two orders of magnitude speedups against the state-of-the-art CPU and GPU-based solutions.

Multi-mode photoluminescence inks based on lanthanide-doped multilevel core-shell microspheres for advanced anti-counterfeiting applications

Development of advanced luminescent nanomaterials and technologies is of great significance for anticounterfeiting applications in global economy, security, and human health. In this work, we have designed two types of anti-counterfeit materials. (1) The dual excitation mode core–shell microsphere is based on SiO2 core, with the GdVO4:Yb,Er upconversion luminescent inner layer, SiO2 shell separator, and Gd2O3:Eu@SiO2 downconversion luminescent layer are sequentially deposited on it via co-precipitation method. The microspheres have a five-layer core–shell structure with an overall diameter of ∼ 146 nm and enables the spatial separation of Er3+ and Eu3+, which prevents the cross-relaxation of Er3+. The microspheres were prepared into an upconversion/downconversion excitation mode luminescent ink, with inkjet-printed patterns that only extract information when excited by 254 nm and 980 nm excitation. (2) The core–shell-shell SiO2@AIPA-S-Si-Tb@SiO2 microspheres have been synthesized by using organic ligands ((HOOC)2C6H3NHCONH(CH2)3Si(OCH2CH3)3) (defined as AIPA-S-Si) connected with Tb3+ ions and silica through covalent bond. The microspheres have a sandwich structure with an overall diameter of ∼ 100 nm and show bright green light. The sandwich structure and five-layer core–shell structure was mixed proportionally to obtain orange-red, white, and yellow emission ink under 254 nm, green emission under 310 nm, and green emission under 980 nm excitation.

High-Level Programming of FPGA-Accelerated Systems with Parallel Patterns

As a result of frequency and power limitations, multi-core processors and accelerators are becoming more and more prevalent in today’s systems. To fully utilize such systems, heterogeneous parallel programming is needed, but this introduces new complexities to the development. High-level frameworks such as SkePU have been introduced to help alleviate these complexities. SkePU is a skeleton programming framework based on a set of programming constructs implementing computational parallel patterns, while presenting a sequential interface to the programmer. Using the various skeleton backends, SkePU programs can execute, without source code modification, on multiple types of hardware such as CPUs, GPUs, and clusters. This paper presents the design and implementation of a new backend for SkePU, adding support for FPGAs. We also evaluate the effect of FPGA-specific optimizations in the new backend and compare it with the existing GPU backend, where the actual devices used are of similar vintage and price point. For simple examples, we find that the FPGA-backend’s performance is similar to that of the existing backend for GPUs, while it falls behind in more complex tasks. Finally, some shortcomings in the backend are highlighted and discussed, along with potential solutions.

Making stateless and stateful network performance measurements unbiased

The Benchmarking Working Group (BMWG) of the Internet Engineering Task Force (IETF) has defined a series of Requests for Comments (RFC) to standardize the benchmarking of network interconnect devices (e.g., bridges, routers, different IPv6 transition solutions). The paper points out that there are cases where the performance results are significantly different when a single IP address pair or multiple IP addresses are used. The cause of this phenomenon is rooted in the recent hardware and software advancements: Receive Side Scaling (RSS) makes it possible to distribute packet processing workload over multiple CPU cores. However, this may be implemented in two ways: the first way only includes the IP addresses into the hash function used to distribute the workload among the CPU cores, whereas the second one also includes the port numbers. RFC 4814 proposed an excellent solution for the second case by recommending the usage of pseudorandom port numbers during benchmarking; however, the first case was not handled properly, because no explicit recommendation was given regarding the usage of multiple IP addresses. This paper attempts to bridge this methodological gap; a practical solution is proposed for using pseudorandom IP addresses in various scenarios including the benchmarking of IPv4 and IPv6 routers and Network Address Translation from IPv6 Clients to IPv4 Servers (stateful NAT64) gateways. Its feasibility is shown by disclosing the details of its implementation in siitperf. Then the proposed solution is validated by both stateless and stateful tests. It is shown that the measurement results of the tests following the proposed solution can better characterize the true performance of the network interconnect devices that follow the first type of RSS implementation than the results of the tests using a single IP address pair.

Parallel GEMM-based convolutions for deep learning on multicore ARM and RISC-V architectures

We present high performance, multi-threaded implementations of three GEMM-based convolution algorithms for multicore processors with ARM and RISC-V architectures. The codes are integrated into CONVLIB, a library that has the following unique features: (1) scripts to automatically generate a key component of GEMM, known as the micro-kernel, which is typically written in assembly language; (2) a modified analytical model to automatically tune the algorithms to the underlying cache architecture; (3) the ability to select four hyper-parameters: micro-kernel, cache parameters, parallel loop, and GEMM algorithm dynamically between calls to the library, without recompiling it; and (4) a driver to identify the best hyper-parameters. In addition, we provide a detailed performance evaluation of the convolution algorithms, on five ARM and RISC-V processors, and we publicly release the codes.

An efficient machine learning based CPU scheduler for heterogeneous multicore processors

An efficient machine learning based CPU scheduler for heterogeneous multicore processors

BIOMETRIC HASHING TEMPLATE PROTECTION

The main motivation of the project defines about the secure authentication of the modules that is multi core authentication of both the face and iris. The goal is to build a system that can handle more process concurrently, improving system performance in general. The system maintains HAAR cascades and Back Propagation Neural Network.The security can be added with Elliptical Curve Cryptography algorithm generation

Parallel SnowModel (v1.0): a parallel implementation of a distributed snow-evolution modeling system (SnowModel)

Abstract. SnowModel, a spatially distributed snow-evolution modeling system, was parallelized using Coarray Fortran for high-performance computing architectures to allow high-resolution (1 m to hundreds of meters) simulations over large regional- to continental-scale domains. In the parallel algorithm, the model domain was split into smaller rectangular sub-domains that are distributed over multiple processor cores using one-dimensional decomposition. All the memory allocations from the original code were reduced to the size of the local sub-domains, allowing each core to perform fewer computations and requiring less memory for each process. Most of the subroutines in SnowModel were simple to parallelize; however, there were certain physical processes, including blowing snow redistribution and components within the solar radiation and wind models, that required non-trivial parallelization using halo-exchange patterns. To validate the parallel algorithm and assess parallel scaling characteristics, high-resolution (100 m grid) simulations were performed over several western United States domains and over the contiguous United States (CONUS) for a year. The CONUS scaling experiment had approximately 70 % parallel efficiency; runtime decreased by a factor of 1.9 running on 1800 cores relative to 648 cores (the minimum number of cores that could be used to run such a large domain because of memory and time limitations). CONUS 100 m simulations were performed for 21 years (2000–2021) using 46 238 and 28 260 grid cells in the x and y dimensions, respectively. Each year was simulated using 1800 cores and took approximately 5 h to run.