TENSILE: A Tensor granularity dynamic GPU memory scheduling method towards multiple dynamic workloads system

Recently, deep learning has been an area of intense research. However, as a kind of computing-intensive task, deep learning highly relies on the scale of GPU memory, which is usually prohibitive and scarce. Although there are some extensive works have been proposed for dynamic GPU memory management, they are hard to be applied to systems with multiple dynamic workloads, such as in-database machine learning systems. In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, considering the multiple dynamic workloads. TENSILE tackled the cold-starting and across-iteration scheduling problem existing in previous works. We implement TENSILE on a deep learning framework built by ourselves and evaluated its performance. The experiment results show that TENSILE can save more GPU memory with less extra time overhead than prior works in both single and multiple dynamic workloads scenarios

Performance analysis and optimization for workflow authorization

Many workflow management systems have been developed to enhance the performance of workflow executions. The authorization policies deployed in the system may restrict the task executions. The common authorization constraints include role constraints, Separation of Duty (SoD), Binding of Duty (BoD) and temporal constraints. This paper presents the methods to check the feasibility of these constraints, and also determines the time durations when the temporal constraints will not impose negative impact on performance. Further, this paper presents an optimal authorization method, which is optimal in the sense that it can minimize a workflow’s delay caused by the temporal constraints. The authorization analysis methods are also extended to analyze the stochastic workflows, in which the tasks’ execution times are not known exactly, but follow certain probability distributions. Simulation experiments have been conducted to verify the effectiveness of the proposed authorization methods. The experimental results show that comparing with the intuitive authorization method, the optimal authorization method can reduce the delay caused by the authorization constraints and consequently reduce the workflows’ response time

Structure Inheritance in Nanoparticle Ink Direct-Writing Processes and Crack-Free Nano-Copper Interconnects Printed by a Single-Run Approach

When nanoparticle conductive ink is used for printing interconnects, cracks and pores are common defects that deteriorate the electrical conductivity of the printed circuits. Influences of the ink solvent, the solid fraction of the ink, the pre-printing treatment and the sintering parameters on the interconnect morphology and conductivity were investigated. It was found that the impacts of all these factors coupled with each other throughout the whole procedure, from the pre-printing to the post-printing processes, and led to a structure inheritance effect. An optimum process route was developed for producing crack-free interconnects by a single-run direct-writing approach using home-made nano-copper ink. A weak gel was promoted in the ink before printing in the presence of long-chain polymers and bridging molecules by mechanical agitation. The fully developed gel network prevented the phase separation during ink extrusion and crack formations during drying. With the reducing agents in the ink and slow evaporation of the ink solvent, compact packing and neck joining of copper nanoparticles were obtained after a two-step sintering process. The crack-free interconnects successfully produced have a surface roughness smaller than 1.5 μm and the square resistances as low as 0.01 Ω/□

Characteristics of T(0, 1) Guided-Wave Point-Focusing Electromagnetic Acoustic Transducer for Pipe Inspection

Electromagnetic acoustic transducers (EMATs) are widely used in the field of non-destructive testing (NDT) and non-destructive evaluation (NDE) due to their advantages of non-contact and applicability in complex environments. However, their low energy-conversion efficiency restricts their application in industrial testing. This work proposes a newly designed focusing EMAT based on the T(0, 1)-wave in a pipe to improve the energy conversion efficiency of EMATs. The focal length and defect width are investigated in this paper, and the advantages of the newly designed point-focusing transducer are shown in comparison with traditional unfocused transducers. Results show that the newly designed pipe focusing EMAT can increase the intensity of the defect reflection wave by around 60%, and the reflected wave intensity can be enhanced by 70% in the experiment when compared to the unfocused one

3D focusing acoustic lens optimization method using multi-factor and multi-level orthogonal test designing theory

Focusing systems that consist of acoustic lenses exhibit higher controllability and focusing intensity when manipulating sound waves. Different parameters have different effects on the performance of the acoustic lens, so analyzing a variety of parameter combinations for the acoustic lens is difficult because of the complexity of the test. Therefore, we report an analytical method for studying the focusing performance of different factors on acoustic lenses at different levels, and the influence degree of each factor is investigated through range analysis. Results show that the factors that have the most significant influence on the focusing intensity, focal-area dimensions, and focal length are the incident sound field’s pressure P0, cell dimension c, and cell edges k, respectively. Moreover, the effects of other parameters, including the biased incident-wave angles, are obtained and analyzed through finite-element simulations. After analyzing and comprehensively comparing the influences of various parameters, the optimal parameter combination is obtained to achieve the best focusing performance of an acoustic lens. The experimental results show that the focusing intensity of the optimized acoustic lens is nearly 90% higher than the non-optimized one, which proves the effectiveness of the orthogonal test-optimization method in this paper

Point-Focusing Shear-Horizontal Guided Wave EMAT Optimization Method Using Orthogonal Test Theory

As a class of devices used in sound detection, electromagnetic acoustic transducers (EMATs) have been widely used in the field of nondestructive testing (NDT) owing to their advantages such as contact-free operation, wide applicability, and high performance. However, their low energy-conversion efficiency is the main drawback that limits usage in industrial testing. In this work, we report the effect of different parameters of the point-focusing shear-horizontal EMAT (PFSH-EMAT) on the signal intensity of the receiving transducer. The impact factors of the focusing transducer are as follows: number of magnets in a row m, fan-shaped periodic permanent magnet (FPPM) remanence magnetization Br, coil width w, coil winding number n, aperture angle θ, focal length lF, lift-off distance hl, excitation current frequency fc, and current amplitude Ic. To improve the analysis efficiency, an L32 (49) orthogonal table is used to investigate the nine different factors at four levels, which can be calculated through finite element simulations. The effect of each factor on the signal intensity is obtained by range analysis, and the optimal combination of these impact factors can be achieved by reasonable level selection. Through range analysis, the influence degree of each factor is obtained, and the parameter combination is optimized by analyzing the test results to improve the PFSH-EMAT’s performance. The experimental results show that the optimized PFSH-EMAT is 170% more efficient than the average of the top-three signal intensities in the orthogonal test, which proves the effectiveness of the proposed optimization method