Parallel Processing Research Articles

Chip-Level Defect Analysis with Virtual Bad Wafers Based on Huge Big Data Handling for Semiconductor Production

Semiconductors continue to shrink in die size because of benefits like cost savings, lower power consumption, and improved performance. However, this reduction leads to more defects due to increased inter-cell interference. Among the various defect types, customer-found defects are the most costly. Thus, finding the root cause of customer-found defects has become crucial to the quality of semiconductors. Traditional methods involve analyzing the pathways of many low-yield wafers. Yet, because of the extremely limited number of customer-found defects, obtaining significant results is difficult. After the products are provided to customers, they undergo rigorous testing and selection, leading to a very low defect rate. However, since the timing of defect occurrence varies depending on the environment in which the product is used, the quantity of defective samples is often quite small. Unfortunately, with such a low number of samples, typically 10 or fewer, it becomes impossible to investigate the root cause of wafer-level defects using conventional methods. This paper introduces a novel approach to finding the root cause of these rare defective chips for the first time in the semiconductor industry. Defective wafers are identified using rare customer-found chips and chip-level EDS (Electrical Die Sorting) data, and these newly identified defective wafers are termed vBADs (virtual bad wafers). The performance of root cause analysis is dramatically improved with vBADs. However, the chip-level analysis presented here demands substantial computing power. Therefore, MPP (Massive Parallel Processing) architecture is implemented and optimized to handle large volumes of chip-level data within a large architecture infrastructure that can manage big data. This allows for a chip-level defect analysis system that can recommend the relevant EDS test and identify the root cause in real time even with a single defective chip. The experimental results demonstrate that the proposed root cause search can reveal the hidden cause of a single defective chip by amplifying it with 90 vBADs, and system performance improves by a factor of 61.

Enhancing RAG Systems: A Survey of Optimization Strategies for Performance and Scalability

Retrieval Augmented Generation (RAG) systems offer significant advancements in natural language processing by combining large language models (LLMs) with external knowledge sources to improve factual accuracy and contextual relevance. However, the computational complexity of RAG pipelines presents challenges in terms of efficiency and scalability. This research paper conducts a comprehensive survey of optimization techniques across four key areas: tokenizer performance, encoder performance, vector database search strategies, and LLM agent integra- tion. The study explores approaches to accelerate tokenization using Rust-based implementations and investigates hardware optimizations like CUDA and Ten- sor cores to boost encoder efficiency. Additionally, it delves into algorithms and indexing strategies for efficient vector database searches and examines meth- ods for optimising the interaction between retrieved knowledge and LLM agents. By analysing recent research and evaluating various optimization strategies, this paper aims to provide valuable insights into enhancing the performance and practicality of RAG systems for real-world applications. Keywords: Retrieval-Augmented Generation, Performance optimization, Tokenization, WordPiece, Rust, FastTokenizer,Encoding, GPU, CUDA, Tensor Cores, Parallel Processing, Vector Databases, Indexing, HNSW, FAISS, DiskANN, LLM Agents, Prompt Engineering, Retriever

SoC-FPGA Based Concept of Hardware Aided Quantum Simulation

Contemporary industry and science expectations towards technological solutions set the bar high. Current approaches to increasing the computing power of standard systems are reaching the limits of physics known to humankind. Fast, programmable systems with relatively low power consumption are a different concept for performing complex calculations. Highly parallel processing opens up a number of possibilities in the context of accelerating calculations. Application of SoC (System On Chip) with FPGA (Field-Programmable Gate Array) enables to delegate of a part of computations to the gates matrix, thereby expediting processing by using parallelization of hardware operations. This paper presents the general concept of using SoC FPGA systems to support CPU (Central Processing Unit) in many modern tasks. While some tasks might be really hard to implement on an FPGA in a reasonable time, the SoC FPGA platform allows for easy low-level interconnections, and with such virtualized access to the hardware computing resources, it is seen as making FPGAs, or hardware in general, more accessible to engineers accustomed to high-level solutions. The concept presented in the article takes into account the limited resources of cheaper educational platforms, which, however, still provide an interesting and alternative hybrid solution to the problem of parallelization and acceleration of data processing. This allows to overcome encountered limitations and maintain the flexibility known from high-level solutions and high performance achieved with low-level programming, without the need for a high financial background.

High-Performance Krawtchouk Polynomials of High Order Based on Multithreading

Orthogonal polynomials and their moments serve as pivotal elements across various fields. Discrete Krawtchouk polynomials (DKraPs) are considered a versatile family of orthogonal polynomials and are widely used in different fields such as probability theory, signal processing, digital communications, and image processing. Various recurrence algorithms have been proposed so far to address the challenge of numerical instability for large values of orders and signal sizes. The computation of DKraP coefficients was typically computed using sequential algorithms, which are computationally extensive for large order values and polynomial sizes. To this end, this paper introduces a computationally efficient solution that utilizes the parallel processing capabilities of modern central processing units (CPUs), namely the availability of multiple cores and multithreading. The proposed multi-threaded implementations for computing DKraP coefficients divide the computations into multiple independent tasks, which are executed concurrently by different threads distributed among the independent cores. This multi-threaded approach has been evaluated across a range of DKraP sizes and various values of polynomial parameters. The results show that the proposed method achieves a significant reduction in computation time. In addition, the proposed method has the added benefit of applying to larger polynomial sizes and a wider range of Krawtchouk polynomial parameters. Furthermore, an accurate and appropriate selection scheme of the recurrence algorithm is introduced. The proposed approach introduced in this paper makes the DKraP coefficient computation an attractive solution for a variety of applications.

Energy transition in Poland and Spain against changes in the EU energy and climate policy

The European Union climate policy and the subsequent energy transition are expected to cause fundamental changes in individual EU countries, their economies, and industrial sectors. Furthermore, in order to achieve them, high financial outlays are essential. The main purpose of the article is to present a comparative analysis of the pace, directions, and main factors of the energy transition in Poland and Spain against the changes in the EU energy and climate policy. In particular, the subject of the study is the changes in the fuel structure of electricity generation (energy mix) and the changes in the concentration of energy generation. The analysis concentrates on Poland and Spain against the background of the group of EU27 countries. The study adopted a long-term perspective (1990–2020). The methodology used taxonomic measures of variability of structures and measures of concentration, as well as measures of descriptive and mathematical statistics. The result of the research proves that the EU common climate and energy policy has explicitly accelerated changes in the energy mix, both in the EU27 and in the studied countries, including those traditionally based on coal. The concentration analysis demonstrated an increase in diversification resulting in a significant increase in the share of renewable and low-carbon sources. The taxonomic analysis additionally proved that there was a parallel process towards making the energy mix of the studied countries more similar to the EU27. The limitation of the research is the adopted triangulation arrangement of the studied structures (EU27, EU15, countries that accessed the EU after 2004), enforced in this pilot research, but possible to expand the field of perception in subsequent research. What serves as a proof of the originality of the study is the fulfilment of research gaps in the long-term study of the degree of changes in the concentration of the energy mix structure of EU countries, and especially the study of the degree of intensity of its similarity. The application value of the study is its use in the energy policies of countries traditionally based on coal for the indication of the taken position, the goals, and ways to achieve them based on the experience of other countries.

A new communication approach to encourage lung cancer screening action in rural eligible populations

ObjectiveThe purpose of this study was to develop an effective communication approach to encourage lung cancer screening action within rural screening-eligible populations. MethodsAn iterative research approach using targeted engagement with the priority population was used. Findings were triangulated through multiple methods, including two surveys and concept testing interviews. The Health Belief Model and the Extended Parallel Process Model served as study frameworks. ResultsInitial findings suggest that threat levels are high in the priority population and an emphasis on barrier mitigation messaging may drive action. Health campaign posters integrating these findings were developed and tested with the priority population. The new health campaign posters were tested against examples of previously used health campaign posters. Findings suggest that the new health campaign posters were more effective in spurring lung cancer screening motivation and intention to act in the priority population compared to current health campaign poster examples. ConclusionMessaging focused on gain-framing, inoculation messaging, and barrier mitigation may be more effective in encouraging lung cancer screening action in rural eligible populations. InnovationThis project outlines a systematic process to developing effective, targeted communication approaches using behavior change and persuasive communication frameworks along with engagement from priority populations.

Strengthening legal preparedness and response within the global health emergency framework: the role of the GHSA Legal Preparedness Action Package*

The COVID-19 pandemic exposed failings in preparedness and response across the world and a shortfall in global health security. The crisis also highlighted shortcomings in global and regional legal frameworks, including the International Health Regulations (2005) and relevant European Union law, as well as in national public health legislation and emergency plans. Lessons learned from the pandemic converge around strengthening preparedness and response capacities at all levels, with special attention to be paid to legal preparedness, a critical component of public health emergency preparedness. Improving legal capacities at country level is one of the strategic priorities set by the Global Health Security Agenda (GHSA), which established a new Legal Preparedness Action Package (LP AP) in order to elevate attention to and the importance of legal preparedness worldwide. Since 2021 the LP AP has been front and centre in international efforts to bridge the legal preparedness gap at national level. In bringing together technical experts in global health law and government health policy officials from a broad range of countries and partners, the LP AP seeks to set a foundation for legal preparedness by developing technical and capacity-building tools, legal guidelines, policy briefs, information and training. This article aims to illustrate and contextualize the work of the GHSA LP AP and raise awareness about the importance of its contribution to enhanced global health security. It also intends to highlight how its work promotes and develops synergies with parallel initiatives, processes and tools, facilitates regime interaction and harmonization of technical assistance and ultimately ensures consistency of action, methodology and approaches.

Design and Implementation of Braun Multiplier using Verilog: Research

Abstract: Multiplication is widely used in places like digital signal processing, image processing, instrumentation that require multilayer components. Carry Save Adder and ripple carry adder both are used in parallel processing architecture. They are widely used for signed multiplication i.e. for both negative and positive numbers. We have now designed a Braun multiplier (BM) to improve the speed, power and area capability. We are using the Xilinx tool to verify Braun multipliers using Verilog. We are taking the following considerations: checking using FPGA based instruments and implementing code using Verilog-VHDL. The adders are integrated into multipliers. The result of this research is to modify BM to improve the performance along with reduction in power consumed and also using less area.

Examining the Role of Information Behavior in Linking Cancer Risk Perception and Cancer Worry to Cancer Fatalism in China: Cross-Sectional Survey Study.

Reducing cancer fatalism is essential because of its detrimental impact on cancer-related preventive behaviors. However, little is known about factors influencing individuals' cancer fatalism in China. With a general basis of the extended parallel process model, this study aims to examine how distinct cancer-related mental conditions (risk perception and worry) and different information behaviors (information seeking vs avoidance) become associated with cancer fatalism, with an additional assessment of the moderating effect of information usefulness. Data were drawn from the Health Information National Trends Survey in China, which was conducted in 2017 (N=2358). Structural equation modeling and bootstrapping methods were performed to test a moderated mediation model and hypothesized relationships. The results showed that cancer risk perception and cancer worry were positively associated with online health information seeking. In addition, cancer worry was positively related to cancer information avoidance. Moreover, online health information seeking was found to reduce cancer fatalism, while cancer information avoidance was positively associated with cancer fatalism. The results also indicated that the perceived usefulness of cancer information moderated this dual-mediation pathway. The national survey data indicate that cancer mental conditions should not be treated as homogeneous entities, given their varying functions and effects. Apart from disseminating useful cancer information to encourage individuals to adaptively cope with cancer threats, we advocate for health communication programs to reduce cancer information avoidance to alleviate fatalistic beliefs about cancer prevention.

CPU 병렬 처리를 이용한 SPH 유체와 단일보드 컴퓨터의 실시간 상호작용 시스템 개발

CPU 병렬 처리를 이용한 SPH 유체와 단일보드 컴퓨터의 실시간 상호작용 시스템 개발

NGS method for parallel processing of high quality, damaged or fragmented input material using target enrichment.

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade and is now commonly used in clinical applications for precision diagnostics. Many disease areas typically involve different kinds of sample specimens, sample qualities and quantities. The quality of the DNA can range from intact, high molecular weight molecules to degraded, damaged and very short molecules. The differences in quality and quantity pose challenges for downstream molecular analyses. To overcome the challenge with the need of different molecular methods for different types of samples, we have developed a joint procedure for preparing enriched DNA libraries from high molecular weight DNA and DNA from formalin-fixed, paraffin-embedded tissue, fresh frozen tissue material, as well as cell-free DNA.

Bioinspired In-Sensor Multimodal Fusion for Enhanced Spatial and Spatiotemporal Association.

Multimodal perception can capture more precise and comprehensive information compared with unimodal approaches. However, current sensory systems typically merge multimodal signals at computing terminals following parallel processing and transmission, which results in the potential loss of spatial association information and requires time stamps to maintain temporal coherence for time-series data. Here we demonstrate bioinspired in-sensor multimodal fusion, which effectively enhances comprehensive perception and reduces the level of data transfer between sensory terminal and computation units. By adopting floating gate phototransistors with reconfigurable photoresponse plasticity, we realize the agile spatial and spatiotemporal fusion under nonvolatile and volatile photoresponse modes. To realize an optimal spatial estimation, we integrate spatial information from visual-tactile signals. For dynamic events, we capture and fuse in real time spatiotemporal information from visual-audio signals, realizing a dance-music synchronization recognition task without a time-stamping process. This in-sensor multimodal fusion approach provides the potential to simplify the multimodal integration system, extending the in-sensor computing paradigm.

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact. Optuna–LightGBM–XGBoost, a novel power and carbon emission relationship model that aims to improve the efficiency of carbon emission monitoring and estimation for power generation companies, is proposed in this paper. Deeply exploring the intrinsic link between power production data and carbon emissions, this model paves a new path for “measuring carbon through electricity”, in contrast to the emission factor method commonly used in China. Unit data from power generation companies are processed into structured tabular data, and a parallel processing framework is constructed with LightGBM and XGBoost, and optimized with the Optuna algorithm. The multilayer perceptron (MLP) is used to fuse features to enhance prediction accuracy by capturing characters that the individual models cannot detect. Simulation results show that Optuna–LightGBM–XGBoost can achieve better performance compared to existing methods. The mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) of the model are 0.652, 0.939, 0.136, and 0.994, respectively. This not only helps governments and enterprises to develop more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for achieving global carbon neutrality goals.

Organization of corticocortical and thalamocortical top-down inputs in the primary visual cortex.

Unified visual perception requires integration of bottom-up and top-down inputs in the primary visual cortex (V1), yet the organization of top-down inputs in V1 remains unclear. Here, we used optogenetics-assisted circuit mapping to identify how multiple top-down inputs from higher-order cortical and thalamic areas engage V1 excitatory and inhibitory neurons. Top-down inputs overlap in superficial layers yet segregate in deep layers. Inputs from the medial secondary visual cortex (V2M) and anterior cingulate cortex (ACA) converge on L6 Pyrs, whereas ventrolateral orbitofrontal cortex (ORBvl) and lateral posterior thalamic nucleus (LP) inputs are processed in parallel in Pyr-type-specific subnetworks (Pyr←ORBvl and Pyr←LP) and drive mutual inhibition between them via local interneurons. Our study deepens understanding of the top-down modulation mechanisms of visual processing and establishes that V2M and ACA inputs in L6 employ integrated processing distinct from the parallel processing of LP and ORBvl inputs in L5.

Non-identical parallel machines batch processing problem to minimize the makespan: Models and algorithms

This paper studies a parallel heterogeneous machine batching and scheduling problem in which weighted jobs are first batched, and the batches are then assigned and sequenced on machines of varying capacities. The duration of a batch is the longest time needed to process a job, and the objective is that of minimizing the makespan, or the sum of the batches durations on the machine finishing last. The authors develop polynomial-size mathematical formulations and a variable neighborhood search metaheuristic. Extensive computational results suggest that a flow-based formulation outperforms a compact formulation, despite its larger number of variables. The metaheuristic is capable of producing high-quality solutions within a limited computing time.

On-demand photonic Ising machine with simplified Hamiltonian calculation by phase encoding and intensity detection

The photonic Ising machine is a new paradigm of optical computing that takes advantage of the unique properties of light wave propagation, parallel processing, and low-loss transmission. Thus, the process of solving combinatorial optimization problems can be accelerated through photonic/optoelectronic devices, but implementing photonic Ising machines that can solve arbitrary large-scale Ising problems with fast speed remains challenging. In this work, we have proposed and demonstrated the Phase Encoding and Intensity Detection Ising Annealer (PEIDIA) capable of solving arbitrary Ising problems on demand. The PEIDIA employs the heuristic algorithm and requires only one step of optical linear transformation with simplified Hamiltonian calculation by encoding the Ising spins on the phase term of the optical field and performing intensity detection during the solving process. As a proof of principle, several 20 and 30-spin Ising problems have been solved with high ground state probability (≥0.97/0.85 for the 20/30-spin Ising model).

Research Paper on Visualization of Sorting Algorithm

In the realm of sorting algorithms, visualization projects serve as educational tools to comprehend and demonstrate various sorting techniques. This paper presents an analysis and review of sorting visualization projects that do not utilize parallelism. By exploring sorting algorithms without parallel processing, this review aims to provide insights into the efficiency, functionality, and visual representation of these algorithms. Understanding sorting algorithms without parallelism contributes to a foundational understanding of their sequential execution and computational complexities. Keywords: Sorting Algorithms, React Visualizer, Selection Sort, Merge Sort, Bubble Sort, Insertion Sort, Heap Sort.

Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms

Abstract. The microprocessor without interlocked piped stages (MIPS) and LoongArch are reduced instruction set computing (RISC) processor architectures, which have advantages in terms of energy consumption and efficiency. There are few studies on the application of MIPS and LoongArch central processing units (CPUs) in geoscientific numerical models. In this study, the Loongson 3A4000 CPU platform with the MIPS64 architecture and the Loongson 3A6000 CPU platform with the LoongArch architecture were used to establish the runtime environment for the air quality modelling system Weather Research and Forecasting–Comprehensive Air Quality Model with extensions (WRF-CAMx) in the Beijing–Tianjin–Hebei region. The results show that the relative errors for the major species (NO2, SO2, O3, CO, PNO3, and PSO4) between the MIPS and X86 benchmark platforms are within ±0.1 %. The maximum mean absolute error (MAE) of major species ranged up to 10−2 ppbV or µg m−3, the maximum root mean square error (RMSE) ranged up to 10−1 ppbV or µg m−3, and the mean absolute percentage error (MAPE) remained within 0.5 %. The CAMx takes about 195 min on the Loongson 3A4000 CPU, 71 min on the Loongson 3A6000 CPU, and 66 min on the Intel Xeon E5-2697 v4 CPU, when simulating a 24 h case with four parallel processes using MPICH. As a result, the single-core computing capability of the Loongson 3A4000 CPU for the WRF-CAMx modelling system is about one-third of the Intel Xeon E5-2697 v4 CPU, and the one of Loongson 3A6000 CPU is slightly lower than that of Intel Xeon E5-2697 v4 CPU; but, the thermal design power (TDP) of Loongson 3A4000 is 40 W, while the TDP of Loongson 3A6000 is 38 W, only about one-fourth of that of Intel Xeon E5-2697 v4, whose TDP is 145 W. The results also verify the feasibility of cross-platform porting and the scientific usability of the ported model. This study provides a technical foundation for the porting and optimization of numerical models based on MIPS, LoongArch, or other RISC platforms.

High viscosity ratio multicomponent flow simulations in porous media using a pseudo-potential central moment lattice Boltzmann method

Multicomponent Immiscible flows within porous media are a pivotal concern in engineering applications, especially in contexts such as oil reservoirs, which exhibit a broad spectrum of properties, including varying viscosities. The precise characterization of flow dynamics, particularly when confronted with a dynamic contact interface within intricate and heterogeneous structures, has remained a significant challenge in the field. This study employs an enhanced Pseudo-potential Central Moment Lattice Boltzmann Method to simulate multicomponent immiscible flows within porous media. The model is implemented through an in-house CUDA code (called Dena) to leverage the parallel processing capabilities of the Lattice Boltzmann Method. In order to assess the accuracy and effectiveness of this method, several test cases are examined, including the Capillary Intrusion Test, the Fingering Process, and the Drainage Process within a real porous medium. Comparative analysis against analytical and experimental results substantiates the model's capabilities and accuracy. Furthermore, the impact of viscosity ratio variations among different flow components within porous media is investigated. With the developed approach, it is possible to span a range of viscosity ratios up to 2132. The findings underscore the model's stability and accuracy in simulating high-viscosity ratio multicomponent fluid flows within porous media.

Design of Approximate Tree Multiplier Using Approximate Compressor for Image Processing Applications

Approximate computing enables high performance and efficiency of execution by using the overhead on compute units of a processor to give high speed. The speed and system delay are inversely proportional, necessitating massive parallel processes with massive hardware and power dissipation. Energy and area-efficient systems can be realized by reducing system accuracy and dependability. Approximate computing techniques can be used to reduce the number of computing resources needed for a given task and improve the performance of a system. This can be accomplished by lowering a computational model's accuracy and decreasing complexity, reducing power consumption, latency, and area requirements. This study examines an electrical circuit that performs multiplication of two equal integers and it proposes the use of a carry-in adder with two approximation compressors to reduce the size, latency, and power consumption while maintaining the same level of accuracy as current designs. The suggested algorithms were implemented in Xilinx Vivado and their effectiveness was evaluated using numerical performance metrics, including error rate, power consumption, delay, and power delay product in output result. The compressors suggested are used to implement 8 × 8 and 16 × 16 Dadda multipliers that perform comparably to state-of-the-art approximation multipliers.