PerfBlower: Quickly Detecting Memory-Related Performance Problems via Amplification

Performance problems in managed languages are extremely difficult to find. Despite many efforts to find those problems, most existing work focuses on how to debug a user-provided test execution in which performance problems already manifest. It remains largely unknown how to effectively find performance bugs before software release. As a result, performance bugs often escape to production runs, hurting software reliability and user experience. This paper describes PerfBlower, a general performance testing framework that allows developers to quickly test Java programs to find memory-related performance problems. PerfBlower provides (1) a novel specification language ISL to describe a general class of performance problems that have observable symptoms; (2) an automated test oracle via emph{virtual amplification}; and (3) precise reference-path-based diagnostic information via object mirroring. Using this framework, we have amplified three different types of problems. Our experimental results demonstrate that (1) ISL is expressive enough to describe various memory-related performance problems; (2) PerfBlower successfully distinguishes executions with and without problems; 8 unknown problems are quickly discovered under small workloads; and (3) PerfBlower outperforms existing detectors and does not miss any bugs studied before in the literature

Numerical and physical simulation of rapid microstructural evolution of gas atomised Ni superalloy powders

The rapid microstructural evolution of gas atomised Ni superalloy powder compacts over timescales of a few seconds was studied using a Gleeble 3500 thermomechanical simulator, finite element based numerical model and electron microscopy. The study found that the microstructural changes were governed by the characteristic temperatures of the alloy. At a temperature below the γ' solvus, the powders maintained dendritic structures. Above the γ' solvus temperature but in the solid-state, rapid grain spheroidisation and coarsening occurred, although the fine-scale microstructures were largely retained. Once the incipient melting temperature of the alloy was exceeded, microstructural change was rapid, and when the temperature was increased into the solid + liquid state, the powder compact partially melted and then re-solidified with no trace of the original structures, despite the fast timescales. The study reveals the relationship between short, severe thermal excursions and microstructural evolution in powder processed components, and gives guidance on the upper limit of temperature and time for powder-based processes if desirable fine-scale features of powders are to be preserved

Prediction of mechanical parameters for low-permeability gas reservoirs in the Tazhong Block and its applications

 A longitudinal distribution profile of the mechanical properties of the formations is important for the safe drilling, successful completion, and development of oil and gas reservoirs. However, the mechanical profile of the carbonate formations from the low-permeability gas reservoirs in the Tazhong (TZ) Block is hard to achieve due to the complex structural and lithological characteristics of the carbonates. In this paper, lab measurements are carried out to determine the physical and mechanical properties of the carbonate rocks of the Yingshan Formation in the TZ Block. Based on this, the relationships among density, the interval transit time and the mechanical parameters of the rocks in the TZ Block are constructed. The constructed relationships are then applied to the well-logging prediction of the mechanical profiles of the carbonate formations. The models are verified through the application to the two wells in the TZ Block, the results show that the relative errors in the predicted mechanical parameters are within 10% indicating the efficiency of the constructed models. The result of this study provides reasonable mechanical parameters for the exploration and development of the carbonate reservoirs in the TZ Block.Cited as: Wan, Y., Zhang, H., Liu, X., Yin, G., Xiong, J., Liang, L. Prediction of mechanical parameters for low-permeability gas reservoirs in the Tazhong Block and its applications. Advances in Geo-Energy Research, 2020, 4(2): 219-228, doi: 10.26804/ager.2020.02.1

Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI

Recently, regularization model-driven deep learning (DL) has gained significant attention due to its ability to leverage the potent representational capabilities of DL while retaining the theoretical guarantees of regularization models. However, most of these methods are tailored for supervised learning scenarios that necessitate fully sampled labels, which can pose challenges in practical MRI applications. To tackle this challenge, we propose a self-supervised DL approach for accelerated MRI that is theoretically guaranteed and does not rely on fully sampled labels. Specifically, we achieve neural network structure regularization by exploiting the inherent structural low-rankness of the $k$-space data. Simultaneously, we constrain the network structure to resemble a nonexpansive mapping, ensuring the network's convergence to a fixed point. Thanks to this well-defined network structure, this fixed point can completely reconstruct the missing $k$-space data based on matrix completion theory, even in situations where full-sampled labels are unavailable. Experiments validate the effectiveness of our proposed method and demonstrate its superiority over existing self-supervised approaches and traditional regularization methods, achieving performance comparable to that of supervised learning methods in certain scenarios