The Strong Scaling Advantage of FPGAs in HPC for N-body Simulations

N-body methods are one of the essential algorithmic building blocks of high-performance and parallel computing. Previous research has shown promising performance for implementing n-body simulations with pairwise force calculations on FPGAs. However, to avoid challenges with accumulation and memory access patterns, the presented designs calculate each pair of forces twice, along with both force sums of the involved particles. Also, they require large problem instances with hundreds of thousands of particles to reach their respective peak performance, limiting the applicability for strong scaling scenarios. This work addresses both issues by presenting a novel FPGA design that uses each calculated force twice and overlaps data transfers and computations in a way that allows to reach peak performance even for small problem instances, outperforming previous single precision results even in double precision, and scaling linearly over multiple interconnected FPGAs. For a comparison across architectures, we provide an equally optimized CPU reference, which for large problems actually achieves higher peak performance per device, however, given the strong scaling advantages of the FPGA design, in parallel setups with few thousand particles per device, the FPGA platform achieves highest performance and power efficiency.

Key properties of inorganic thermoelectric materials – tables (version 1)

This paper presents tables of key thermoelectric properties, which define thermoelectric conversion efficiency, for a wide range of inorganic materials. The 12 families of materials included in these tables are primarily selected on the basis of well established, internationally-recognised performance and their promise for current and future applications: Tellurides, Skutterudites, Half Heuslers, Zintls, Mg-Sb Antimonides, Clathrates, FeGa3–type materials, Actinides and Lanthanides, Oxides, Sulfides, Selenides, Silicides, Borides and Carbides. As thermoelectric properties vary with temperature, data are presented at room temperature to enable ready comparison, and also at a higher temperature appropriate to peak performance. An individual table of data and commentary are provided for each family of materials plus source references for all the data.

Striking a Balance between Centralized and Decentralized Decision Making: A School-Based Management Practice for Optimum Performance

Purpose of the Study: School decision-making promotes school autonomy and success. Today’s contemporary approach supports the idea that operative school functioning and development are characteristically accomplished when there is decentralized decision-making. Hence, the purpose of the study is to find the balance between decision-making in a centralized and decentralized structure in a school based system. Methodology: An extensive search of major databases was undertaken, which identified 35,822 studies on the subject, wherein 9 met the inclusion criteria. Employing a systematic literature review, data were extracted and analyzed using thematic analysis. Two themes arose from the analysis of the studies, such as decision-making as a school-based management practice and decision-making towards school performance. Main Findings: Studies on decision-making in school management from a sociological approach. It also highlights the need to mix centralized and decentralized techniques to improve education. Following are some debate points that might want more research: (1) school principal decision-making and (2) school running expense and spending decision-making. Research Implications: The study's findings will aid in improving staff performance and community comprehension of schooling. Increased participation of internal and external stakeholders can boost school autonomy and accountability. The novelty of the study: As a school-based management technique, the correct balance of centralized and decentralized decision-making might enable schools to function at their best while corporations attain peak performance.

Erex Male Enhancement REVIEW v1

➤ Product Name — Erex Male Enhancement➤ Composition — Increase Peak Performance & Increase Blood Flow!➤ Side-Effects — NA➤ Availability — Online➤ Rating — ⭐⭐⭐⭐⭐➤ Price (for Sale) Buy Now Here — https://www. erexmaleenhancement.com➤ [MORE INFO] - FACEBOOK

The New Strategy of Nike Corporate Communication in Efforts to Build a New Corporate Image

This study aims to examine the uniqueness of the corporate communication carried out by Nike when the well-known brand competes to sign a contract with athletes who reach their peak performance to promote its apparel. However, Nike signs contracts with players when they are still rising stars and then leaves them. Nike does something that well-known brands do not usually do. Normally, they compete to attract stars who are in their golden age. This research was conducted using a qualitative approach through case study research. The materials of this research were obtained by conducting a literature review, matters related to the Nike author's boredom collected from various sources, and reviewed using corporate communication logic. The results of this study indicate that Nike's steps are aimed at creating a new impression without leaving the old self-image.

Stride Velocity 95th Centile: Insights into Gaining Regulatory Qualification of the First Wearable-Derived Digital Endpoint for use in Duchenne Muscular Dystrophy Trials

In 2019, stride velocity 95th centile (SV95C) became the first wearable-derived digital clinical outcome assessment (COA) qualified by the European Medicines Agency (EMA) for use as a secondary endpoint in trials for Duchenne muscular dystrophy. SV95C was approved via the EMA’s qualification pathway for novel methodologies for medicine development, which is a voluntary procedure for assessing the regulatory acceptability of innovative methods used in pharmaceutical research and development. SV95C is an objective, real-world digital ambulation measure of peak performance, representing the speed of the fastest strides taken by the wearer over a recording period of 180 hours. SV95C is correlated with traditional clinic-based assessments of motor function and has greater sensitivity to clinical change over 6 months than other wearable-derived stride variables, for example, median stride length or velocity. SV95C overcomes many limitations of episodic, clinic-based motor function testing, allowing the assessment of ambulation ability between clinic visits and under free-living conditions. Here we highlight considerations and challenges in developing SV95C using evidence generated by a high-performance wearable sensor. We also provide a commentary of the device’s technical capabilities, which were a determining factor in the regulatory approval of SV95C. This article aims to provide insights into the methods employed, and the challenges faced, during the regulatory approval process for researchers developing new digital tools for patients with diseases that affect motor function.

Mental Readiness for Frontline Workers in Homelessness Services in Ottawa, Canada

This study investigated factors related to competency by assessing the mental readiness among highly recognized frontline workers in homelessness services (FWHSs) by means of self-completed questionnaires. A total of 35 highly respected FWHSs in Ottawa, Canada were identified by their peers and supervisors as “exceptional” for various specialty areas: addictions, mental health, hoarding, trauma and post-traumatic stress disorder (PTSD). An Operational Readiness Framework was used to examine how FWHSs perform at their best in challenging situations. A series of questionnaires were completed at a Think Tank to determine their mental readiness before, during and after challenging situations. Quantitative and qualitative analyses of mental readiness were performed to prioritize identified challenges. The study findings were then compared to the “Wheel of Excellence” based on results from elite athletes and other high performers such as surgeons, police, and air traffic controllers. The analysis revealed that mental readiness is required to achieve peak performance in addressing the challenges of homelessness. The balance between readiness (physical, technical and mental) and performance contributed to their competency and resiliency. Common elements of success were found: commitment, self-belief, positive imagery, mental preparation, full focus, distraction control and constructive evaluation. This investigation confirmed many similarities in mental readiness practices engaged by excellent FWHSs and other top professionals. This study offered, for the first time, a comprehensive understanding of specific high-performance readiness practices through a streetwise, frontline-worker perspective. Practical recommendations for training and assessment were provided relevant to excellence in homelessness services.

Language Models for the Prediction of SARS-CoV-2 Inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ~9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

PhotoNs-GPU: A GPU accelerated cosmological simulation code

We present a GPU-accelerated cosmological simulation code, PhotoNs-GPU, based on an algorithm of Particle Mesh Fast Multipole Method (PM-FMM), and focus on the GPU utilization and optimization. A proper interpolated method for truncated gravity is introduced to speed up the special functions in kernels. We verify the GPU code in mixed precision and different levels of theinterpolated method on GPU. A run with single precision is roughly two times faster than double precision for current practical cosmological simulations. But it could induce an unbiased small noise in power spectrum. Compared with the CPU version of PhotoNs and Gadget-2, the efficiency of the new code is significantly improved. Activated all the optimizations on the memory access, kernel functions and concurrency management, the peak performance of our test runs achieves 48% of the theoretical speed and the average performance approaches to ∼35% on GPU.