Parallel Threads Research Articles

Top Graduate Zhang Xie: The Earliest Extant Chinese Southern Play

Top Graduate Zhang Xie: The Earliest Extant Chinese Southern Play

Large-Scale Defect Clusters with Hexagonal Honeycomb-like Arrangement in Ammonothermal GaN Crystals

In this paper, we investigate, using X-ray Bragg diffraction imaging and defect selective etching, a new type of extended defect that occurs in ammonothermally grown gallium nitride (GaN) single crystals. This hexagonal “honeycomb” shaped defect is composed of bundles of parallel threading edge dislocations located in the corners of the hexagon. The observed size of the honeycomb ranges from 0.05 mm to 2 mm and is clearly correlated with the number of dislocations located in each of the hexagon’s corners: typically ~5 to 200, respectively. These dislocations are either grouped in areas that exhibit “diameters” of 100–250 µm, or they show up as straight long chain alignments of the same size that behave like limited subgrain boundaries. The lattice distortions associated with these hexagonally arranged dislocation bundles are extensively measured on one of these honeycombs using rocking curve imaging, and the ensemble of the results is discussed with the aim of providing clues about the origin of these “honeycombs”.

SOLO-SLAM: A Parallel Semantic SLAM Algorithm for Dynamic Scenes.

Simultaneous localization and mapping (SLAM) is a core technology for mobile robots working in unknown environments. Most existing SLAM techniques can achieve good localization accuracy in static scenes, as they are designed based on the assumption that unknown scenes are rigid. However, real-world environments are dynamic, resulting in poor performance of SLAM algorithms. Thus, to optimize the performance of SLAM techniques, we propose a new parallel processing system, named SOLO-SLAM, based on the existing ORB-SLAM3 algorithm. By improving the semantic threads and designing a new dynamic point filtering strategy, SOLO-SLAM completes the tasks of semantic and SLAM threads in parallel, thereby effectively improving the real-time performance of SLAM systems. Additionally, we further enhance the filtering effect for dynamic points using a combination of regional dynamic degree and geometric constraints. The designed system adds a new semantic constraint based on semantic attributes of map points, which solves, to some extent, the problem of fewer optimization constraints caused by dynamic information filtering. Using the publicly available TUM dataset, SOLO-SLAM is compared with other state-of-the-art schemes. Our algorithm outperforms ORB-SLAM3 in accuracy (maximum improvement is 97.16%) and achieves better results than Dyna-SLAM with respect to time efficiency (maximum improvement is 90.07%).

Implementation of a program for real-time processing and visualization of LIDAR scan data in the LabVIEW programming environment

The features of development and optimization of software for real-time LIDAR data processing are considered. The advantages of the LabVIEW graphical development environment for creating highly optimized applications using parallel execution threads and pipelined data processing are shown.

Multi-Interval DomLock: Toward Improving Concurrency in Hierarchies

Locking has been a predominant technique depended upon for achieving thread synchronization and ensuring correctness in multi-threaded applications. It has been established that the concurrent applications working with hierarchical data witness significant benefits due to multi-granularity locking (MGL) techniques compared to either fine- or coarse-grained locking. The de facto MGL technique used in hierarchical databases is intention locks , which uses a traversal-based protocol for hierarchical locking. A recent MGL implementation, dominator-based locking (DomLock), exploits interval numbering to balance the locking cost and concurrency and outperforms intention locks for non-tree-structured hierarchies. We observe, however, that depending upon the hierarchy structure and the interval numbering, DomLock pessimistically declares subhierarchies to be locked when in reality they are not. This increases the waiting time of locks and, in turn, reduces concurrency. To address this issue, we present Multi-Interval DomLock (MID), a new technique to improve the degree of concurrency of interval-based hierarchical locking. By adding additional intervals for each node, MID helps in reducing the unnecessary lock rejections due to false-positive lock status of sub-hierarchies. Unleashing the hidden opportunities to exploit more concurrency allows the parallel threads to finish their operations quickly, leading to notable performance improvement. We also show that with sufficient number of intervals, MID can avoid all the lock rejections due to false-positive lock status of nodes. MID is general and can be applied to any arbitrary hierarchy of trees, Directed Acyclic Graphs (DAGs) , and cycles. It also works with dynamic hierarchies wherein the hierarchical structure undergoes updates. We illustrate the effectiveness of MID using STMBench7 and, with extensive experimental evaluation, show that it leads to significant throughput improvement (up to 141%, average 106%) over DomLock.

Multicolor reordering for computing moments in particle-in-cell plasma simulations

Thread parallelism in the computation of the current density/charge density in particle-in-cell plasma simulations has been performed by using the reduction operation conventionally, which is known to have a larger computational overhead with a larger number of threads. In the present study, two types of multicolor reordering, i.e., loop striding and loop tiling/blocking are introduced for a particle shape function with an arbitrary degree, which is free from the reduction operation. The present performance measurement result suggests that the loop tiling is superior to the loop striding.

A Unified Framework Integrating Decision Making and Trajectory Planning Based on Spatio-Temporal Voxels for Highway Autonomous Driving

Intelligent decision making and efficient trajectory planning are closely related in autonomous driving technology, especially in highway environment full of dynamic interactive traffic participants. This work integrates them into a unified hierarchical framework with long-term behavior planning (LTBP) and short-term dynamic planning (STDP) running in two parallel threads with different horizon, consequently forming a closed-loop maneuver and trajectory planning system that can react to the dynamic environment effectively and efficiently. In LTBP, a novel voxel structure and the ‘voxel expansion’ algorithm are proposed for the generation of driving corridors in 3D configuration, which involves the prediction states of surrounding vehicles. By using Dijkstra search, the maneuver with minimal cost is determined in form of voxel sequences, then a quadratic programming (QP) problem is constructed for solving the optimal trajectory. And in STDP, another small-scaled QP problem is performed to track or adjust the reference trajectory from LTBP in response to the dynamic obstacles. Meanwhile, a Responsibility-Sensitive Safety (RSS) Checker keeps running at high frequency for real-time feedback to ensure security. Experiments on real data collected in different highway scenarios demonstrate the effectiveness and efficiency of our work.

Evaluation of Immediately Loaded Parallel Conical Connection Implants with Platform Switch in the Maxillary Esthetic Zone: A Prospective Clinical Study

To assess immediately loaded parallel conical connection (Nobel Biocare) implants with platform switch design in the maxillary esthetic zone for soft and hard tissue changes. A total of 20 patients (n = 20) underwent prosthetic replacement of the missing maxillary anterior tooth, with an immediately loaded parallel conical connection implant (Noble Biocare, Sweden) having a platform switch design. The size of the implant was 3.75 mm in width and 13 mm in length for all patients and placement followed a standardized surgical protocol. Postoperatively, acrylic provisionalization was done within 48 hours followed by a definitive zirconia prosthesis in the 3rd month. Clinically and radiographically, the implants were evaluated for hard tissue (bone density, implant stability, crestal bone loss) and soft tissue changes (mucosal thickness-MT, sulcus probing depth-PD, bleeding on probing-BOP, width of keratinized gingiva-KG) at baseline till 36 months with follow-up intervals after loading. All patients showed uneventful healing. The difference in implant stability and density scores was significant (p <0.05*) from baseline to 36 months indicating bone formation and osseointegration of the implant. Bleeding on probing was not observed, and probing depth remained within the acceptable range (≤5 mm) at all time intervals after loading. The marginal bone loss was minimal (≤0.2 mm annually) with the absence of implant mobility and without any peri-implant radiolucency. The thickness of the gingiva (3.47 ± 0.34 mm) and width of keratinized gingiva (2.46 ± 0.39 mm) remained within reasonable limits at the 36th month with acceptable esthetic appearance. In the present study, immediate loading of Nobel parallel conical connection implant in the maxillary anterior region provided adequate primary stability, minimal marginal bone loss, and increased bone density indicating earlier osseointegration. Decreased probing depth, absence of bleeding on probing, and adequate tissue collar at the neck showed better soft tissue emergence in the esthetic zone. The platform switch design demonstrated promising results and therefore can be used as an alternative to the conventional method. The present study results suggest that parallel conical connection implants (Nobel Biocare) with TiUnite surface, built-in platform switch combined with conical connection interface, parallel walled body, tapered apex, and double threads from tip to platform are all designed to provide high primary stability and support immediate function protocol, hence can be used flexibly in different bone densities.

WIELOPOZIOMOWA INTEGRACJA PROJEKTOWANIA A ZAGADNIENIE JAKOŚCI ŻYCIA W PRZESTRZENI ZURBANIZOWANEJ

The essence of urban design is the transformation of the environment in a way that maximizes the quality of human life. The tools to achieve this goal must cope with the complexity of natural and urbanized spaces, and designers use them to anticipate future environmental states. The article presents theoretical considerations, supplemented by the practical implementation of the area in the Swarzędz community, on the relationship between the organization of urban design in the context of multi-level integration (planning, design) and the emergence of a high-quality urban environment. This process requires controlling many parallel threads between which there are dynamic changes in the relationship - the holarchy.

The Accuracy Comparison Between Word2Vec and FastText On Sentiment Analysis of Hotel Reviews

Word embedding vectorization is more efficient than Bag-of-Word in word vector size. Word embedding also overcomes the loss of information related to sentence context, word order, and semantic relationships between words in sentences. Several kinds of Word Embedding are often considered for sentiment analysis, such as Word2Vec and FastText. Fast Text works on N-Gram, while Word2Vec is based on the word. This research aims to compare the accuracy of the sentiment analysis model using Word2Vec and FastText. Both models are tested in the sentiment analysis of Indonesian hotel reviews using the dataset from TripAdvisor.Word2Vec and FastText use the Skip-gram model. Both methods use the same parameters: number of features, minimum word count, number of parallel threads, and the context window size. Those vectorizers are combined by ensemble learning: Random Forest, Extra Tree, and AdaBoost. The Decision Tree is used as a baseline for measuring the performance of both models. The results showed that both FastText and Word2Vec well-to-do increase accuracy on Random Forest and Extra Tree. FastText reached higher accuracy than Word2Vec when using Extra Tree and Random Forest as classifiers. FastText leverage accuracy 8% (baseline: Decision Tree 85%), it is proofed by the accuracy of 93%, with 100 estimators.&#x0D;

Sim[formula omitted]PIM: A complete simulation framework for Processing-in-Memory

With the help of modern memory integration technologies, Processing-in-Memory (PIM) has emerged as a practical approach to mitigate the memory wall while improving performance and energy efficiency in contemporary applications. Since these designs encompass accelerating and increasing the efficiency of critical specific and general-purposed applications, it is expected that these accelerators will be coupled to existing systems and consequently with systems capable of multi-thread computing. However, there is a lack of tools capable of quickly simulating different PIMs designs and their suitable integration with other hosts. This gap is even worse when considering simulations of multi-core systems. This work presents Sim2PIM, a Simple Simulator for PIM devices that seamlessly integrates any PIM architecture with the host processor and memory hierarchy. The framework simulation achieves execution speeds and accuracy on par with the perf tool on host code, less than 10% run-time overhead, and around 2% difference in metrics. Additionally, by exploring the thread parallelism in the application and utilizing the host hardware, Sim2PIM can achieve more than 8× simulation speedup compared to a sequential simulation and orders of magnitude compared to other simulators. Sim2PIM is available to download at https://pim.computer/.

Fast and robust active camera relocalization in the wild for fine-grained change detection

Active camera relocalization (ACR) is an important and challenging task, whose feasibility and success highly depend on illumination consistency and convergence speed. If under varied lighting conditions in outdoor scenes, however, both the convergence and accuracy of ACR cannot be guaranteed. In this paper, we propose a fast and robust ACR scheme, namely rACR, that works well under highly varied illuminations. To achieve robustness to lighting variations, rather than using 2D feature matching, we rely on 3D point clouds, acquired by a visual SLAM engine (VSE), to register the current and reference camera coordinate frames. We present a scale-aware point cloud matching function that is minimized by a two-stage coarse-to-fine method, i.e., fast alignment considering only geometric error at first, followed by fine-grained alignment optimizing both geometric, photometric errors and the poses of VSE keyframes. The two aligned point clouds with equalized scales help to bridge current and reference observations, avoiding 2D feature matching that are sensitive to large lighting variances, and can directly generate effective camera pose adjustments. Moreover, to achieve fast convergence speed, we implement the above algorithm with a parallel scheme, which is specifically composed of an initialization procedure and three parallel threads, i.e., VSE thread, pose alignment thread, and pose adjustment thread. Extensive experiments show that, rACR has much higher robustness to lighting variations and 5× faster convergence rate over state-of-the-art methods, thus significantly improves its feasibility in real-world fine-grained change detection tasks in the wild.

Uncertainty quantification of aeroelastic wings flutter using an optimized machine learning approach

This study outlines the flutter characteristics of aeroelastic wings under unsteady aerodynamic loading based on an efficient support vector machine assisted k-method. First, the aeroelastic wing flutter speed and flutter frequency are obtained using k-method. Then, the uncertain input parameters distribution is modeled by probability density functions. These parameters are propagated to the aeroelastic wing equations. The Monte Carlo simulation using 12 parallel logical threads is carried out to obtain the flutter speed and the flutter frequency distribution. An optimal robust surrogate model is trained by limited numbers of input and output using support vector machine. Monte Carlo simulation is also carried out in conjunction with the machine learning based k-method computational framework for obtaining the complete probabilistic description of flutter speed and flutter frequency. The coupled support vector regression based k-method is a novel approach that is first used in the aeroelastic wings flutter. The present method is found to reduce the computational time and cost significantly without compromising the accuracy of results.

A Task-Based Approach to Parallel Restricted Hartree-Fock Calculations.

In recent years, parallelism via multithreading has become extremely important to the optimization of high-performance electronic structure theory codes. Such multithreading is generally achieved via OpenMP constructs, using a fork-join threading model to enable thread-level data parallelism within the code. An alternative approach to multithreading is task-based parallelism, which displays multiple benefits relative to fork-join thread parallelism. A novel Restricted Hartree-Fock (RHF) algorithm, utilizing task-based parallelism to achieve optimal performance, was developed and implemented into the JuliaChem electronic structure theory software package. The new RHF algorithm utilizes a unique method of shell quartet batch creation, enabling construction and distribution of fine-grained shell quartet batches in a load-balanced manner using the Julia task construct. These shell quartet batches are then distributed statically across message-passing interface (MPI) ranks and dynamically across threads within an MPI rank, requiring no explicit inter-rank or interthread synchronization to do so. Compared to the hybrid MPI/OpenMP RHF algorithm present in the GAMESS software package, the task-based algorithm demonstrates speedups of up to ∼40% for systems in the S22(3) test set of molecules, with system sizes up to ∼1000 basis functions. The JuliaChem algorithm demonstrates the viability of both the task-based parallelism model and the Julia programming language for construction of performant electronic structure theory codes targeting systems of a size of chemical interest.

Visual-Inertial Image-Odometry Network (VIIONet): A Gaussian process regression-based deep architecture proposal for UAV pose estimation

This study estimates the pose of Unmanned Aerial Vehicle (UAV) through artificial intelligence-based approaches and combines visual-inertial information in a different way than previous studies. For an effective fusion, the inertial data between both frames is normalized after denoising with the Savitzky-Golay technique and finally converted from numerical value to image. To strengthen these inertial image features with the change of motion between two frames, frames of Optical Flow (OF) are obtained and OF frames are combined with inertial images. Simultaneously, a parallel thread combines this OF frame with two consecutive raw frames. After features are extracted from inertial and camera data via Inception-v3, these features are fused and actual UAV poses are estimated via Gaussian Process Regression (GPR). Thanks to the smoothing process applied to these estimated values, a more stable pose estimation is provided. This proposed method is applied to the EuRoC dataset and our dataset produced in the Gazebo environment. The pose estimation results reveal that the proposed method has high performance compared to many previous studies.

High-throughput production of kilogram-scale nanofibers by Kármán vortex solution blow spinning.

The interaction between gas flow and liquid flow, governed by fluid dynamic principles, is of substantial importance in both fundamental science and practical applications. For instance, a precisely designed gas shearing on liquid solution may lead to efficacious production of advanced nanomaterials. Here, we devised a needleless Kármán vortex solution blow spinning system that uses a roll-to-roll nylon thread to deliver spinning solution, coupled with vertically blowing airflow to draw high-quality nanofibers with large throughput. A wide variety of nanofibers including polymers, carbon, ceramics, and composites with tunable diameters were fabricated at ultrahigh rates. The system can be further upgraded from single thread to multiple parallel threads and to the meshes, boosting the production of nanofibers to kilogram scale without compromising their quality.

High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS

We evolve PyDTNN, a framework for distributed parallel training of Deep Neural Networks (DNNs), into an efficient inference tool for convolutional neural networks. Our optimization process on multicore ARM processors involves several high-level transformations of the original framework, such as the development and integration of Cython routines to exploit thread-level parallelism; the design and development of micro-kernels for the matrix multiplication, vectorized with ARM’s NEON intrinsics, that can accommodate layer fusion; and the appropriate selection of several cache configuration parameters tailored to the memory hierarchy of the target ARM processors.Our experiments evaluate both inference throughput (measured in processed images/s) and inference latency (i.e., time-to-response) as well as energy consumption per image when varying the level of thread parallelism and the processor power modes. The experiments with the new inference engine are reported for the ResNet50 v1.5 model on the ImageNet dataset from the MLPerf suite using the ARM v8.2 cores in the NVIDIA Jetson AGX Xavier board. These results show superior performance compared with the well-spread TFLite from Google and slightly inferior results when compared with ArmNN, the native library from ARM for DNN inference.

SparseP

Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures place simple cores close to DRAM banks. Recent research demonstrates that they can yield significant performance and energy improvements in parallel applications by alleviating data access costs. Real PIM systems can provide high levels of parallelism, large aggregate memory bandwidth and low memory access latency, thereby being a good fit to accelerate the Sparse Matrix Vector Multiplication (SpMV) kernel. SpMV has been characterized as one of the most significant and thoroughly studied scientific computation kernels. It is primarily a memory-bound kernel with intensive memory accesses due its algorithmic nature, the compressed matrix format used, and the sparsity patterns of the input matrices given. This paper provides the first comprehensive analysis of SpMV on a real-world PIM architecture, and presents SparseP, the first SpMV library for real PIM architectures. We make three key contributions. First, we implement a wide variety of software strategies on SpMV for a multithreaded PIM core, including (1) various compressed matrix formats, (2) load balancing schemes across parallel threads and (3) synchronization approaches, and characterize the computational limits of a single multithreaded PIM core. Second, we design various load balancing schemes across multiple PIM cores, and two types of data partitioning techniques to execute SpMV on thousands of PIM cores: (1) 1D-partitioned kernels to perform the complete SpMV computation only using PIM cores, and (2) 2D-partitioned kernels to strive a balance between computation and data transfer costs to PIM-enabled memory. Third, we compare SpMV execution on a real-world PIM system with 2528 PIM cores to an Intel Xeon CPU and an NVIDIA Tesla V100 GPU to study the performance and energy efficiency of various devices, i.e., both memory-centric PIM systems and conventional processor-centric CPU/GPU systems, for the SpMV kernel. SparseP software package provides 25 SpMV kernels for real PIM systems supporting the four most widely used compressed matrix formats, i.e., CSR, COO, BCSR and BCOO, and a wide range of data types. SparseP is publicly and freely available at https://github.com/CMU-SAFARI/SparseP. Our extensive evaluation using 26 matrices with various sparsity patterns provides new insights and recommendations for software designers and hardware architects to efficiently accelerate the SpMV kernel on real PIM systems.

Parallel domain discretization algorithm for RBF-FD and other meshless numerical methods for solving PDEs

In this paper, we present a novel parallel dimension-independent node positioning algorithm that is capable of generating nodes with variable density, suitable for meshless numerical analysis. A very efficient sequential algorithm based on Poisson disc sampling is parallelized for use on shared-memory computers, such as the modern workstations with multi-core processors. The parallel algorithm uses a global spatial indexing method with its data divided into two levels, which allows for an efficient multi-threaded implementation. The addition of bootstrapping enables the algorithm to use any number of parallel threads while remaining as general as its sequential variant. We demonstrate the algorithm performance on six complex 2- and 3-dimensional domains, which are either of non rectangular shape or have varying nodal spacing or both. We perform a run-time analysis of the algorithm, to demonstrate its ability to reach high speedups regardless of the domain and to show how well it scales on the experimental hardware with 16 processor cores. We also analyse the algorithm in terms of the effects of domain shape, quality of point placement, and various parallelization overheads.

Accelerating approximate matrix multiplication for near-sparse matrices on GPUs

Although the matrix multiplication plays a vital role in computational linear algebra, there are few efficient solutions for matrix multiplication of the near-sparse matrices. The Sparse Approximate Matrix Multiply (SpAMM) is one of the algorithms to fill the performance gap neglected by traditional optimizations for dense/sparse matrix multiplication. However, existing SpAMM algorithms fail to exploit the performance potential of GPUs for acceleration. In this paper, we present cuSpAMM, the first parallel SpAMM algorithm optimized for multiple GPUs. Several performance optimizations have been proposed, including algorithm re-design to adapt to the thread parallelism, blocking strategies for memory access optimization, and the acceleration with the tensor core. In addition, we scale cuSpAMM to run on multiple GPUs with an effective load balance scheme. We evaluate cuSpAMM on both synthesized and real-world datasets on multiple GPUs. The experiment results show that cuSpAMM achieves significant performance speedup compared to vendor optimized cuBLAS and cuSPARSE libraries.