High-Performance In-Memory OLTP via Coroutine-to-Transaction

Data stalls are a major overhead in main-memory database engines due to the use of pointer-rich data structures. Lightweight coroutines ease the implementation of software prefetching to hide data stalls by overlapping computation and asynchronous data prefetching. Prior solutions, however, mainly focused on (1) individual components and operations and (2) intra-transaction batching that requires interface changes, breaking backward compatibility. It was not clear how they apply to a full database engine and how much end-to-end benefit they bring under various workloads. This thesis presents CoroBase, a main-memory database engine that tackles these challenges with a new coroutine-to-transaction paradigm. Coroutine-to-transaction models transactions as coroutines and thus enables inter-transaction batching, avoiding application changes but retaining the benefits of prefetching. We show that on a 48-core server, CoroBase can perform close to 2× better for read-intensive workloads and remain competitive for workloads that inherently do not benefit from software prefetching

Thermal effects on high-frequency magnetic-field-induced martensite reorientation in ferromagnetic shape memory alloys: An experimental and theoretical investigation

Ferromagnetic Shape Memory Alloys (FSMAs) exhibit large strains by the magnetic-field-induced martensite reorientation. But, due to the high-frequency field-induced cyclic frictional martensite twin boundary motion in FSMAs, the dissipation heat can cause a large temperature rise. Thus, the output strain amplitude of FSMAs would decrease significantly if the temperature increases to be high enough to trigger the Martensite-Austenite phase transformation. Such thermal effects on the dynamic responses of FSMAs are unclear in literature because most existing dynamic experiments were performed only for a short-time period (a few seconds) to avoid the temperature rise. In this paper, systematic long-time experiments (>100 s) on a Ni-Mn-Ga single crystal are conducted at various levels of magnetic field frequency, initial compressive stress and ambient airflow velocity. It is found that, during the long-time actuation, the specimen temperature increases and then saturates at a certain level (stable temperature) while the strain oscillation evolves to a stable cycle; both the stable temperature and the stable strain amplitude depend on the frequency, the stress level and the heat exchange condition (i.e., ambient airflow velocity). Particularly, when the specimen temperature reaches a critical level to partially transform the martensite to the austenite, the output strain amplitude reduces suddenly because of less martensite reorientation. Changing the ambient heat-exchange condition (by the airflow) can modify the specimen temperature evolution to avoid the phase transformation, but it also changes the behaviors of the martensite reorientation that is sensitive to temperature. Eventually, the output strain amplitude depends on the airflow velocity non-monotonically, i.e., there exists a critical heat exchange condition to achieve the maximum stable strain amplitude. Based on the systematic experiments and a simplified one-dimensional heat-transfer model, the critical condition can be determined. The new experimental phenomena of the thermal effects can be well understood and described by the heat-transfer model. Further, instead of avoiding the temperature rise and the phase transformation, we propose to take advantage of the interaction between the temperature-induced phase transformation and the magnetic-field-induced martensite reorientation to develop a special “isothermal” FSMA actuator with a tunable output strain amplitude and a constant working temperature. This paper provides systematic experimental data and theoretical analysis for understanding the thermo-magneto-mechanical coupling in FSMAs and developing reliable high-frequency long-time running FSMA-actuators

Coexistence and compatibility of martensite reorientation and phase transformation in high-frequency magnetic-field-induced deformation of Ni-Mn-Ga single crystal

High-frequency magnetic-field-induced Martensite Reorientation (MR) is one of the most important advantages of Ferromagnetic Shape Memory Alloys (FSMAs), but its stability is threatened by dissipation heat accumulation (“self-heating”) of cyclic frictional twin boundary motion, which can cause temperature-induced Phase Transformation (PT) and reduce the output strain amplitude significantly. In this paper, the interaction of the temperature-induced PT and the magnetic-field-induced MR during high-frequency magnetic actuation on FSMA is studied with in-situ observations of local-strain evolution in conjunction with microstructure compatibility analysis. Based on the nominal strain and temperature responses and the corresponding local-strain maps, it is revealed that, when the temperature-induced PT takes place during the high-frequency field-induced MR, the specimen is divided into three zones: non-active austenite zone (with a constant deformation), active martensite zone (with cyclic deformations of MR) and buffering needle zone (interfacial zone) with a fine-needle-twin structure which plays an important role in maintaining the compatibility between austenite and martensite zones with different cyclic deformations during the dynamic loading. A novel mechanism is revealed that, under the magnetic actuation with changing ambient airflow, the “self-heating” temperature-driven phase boundary motion and the magnetic-field-driven twin boundary motion can coexist, because the specimen needs to self-organize the different phases/variants to satisfy all the thermo-magneto-mechanical boundary conditions. Taking advantage of this mechanism, the volume fractions of austenite and martensite zones can be adjusted with changing ambient airflow velocity, which provides an effective way to tune the nominal output strain amplitude (from 1% to 6% in the current study) while the working temperature is kept almost constant (around Ms and Mf)

Dynamic motion of polar skyrmions in oxide heterostructures

Polar skyrmions have been widely investigated in oxide heterostructure recently, due to their exotic properties and intriguing physical insights. Meanwhile, so far, the external field-driven motion of the polar skyrmion, akin to the magnetic counterpart, has yet to be discovered. Here, using phase-field simulations, we demonstrate the dynamic motion of the polar skyrmions with integrated external thermal, electrical, and mechanical stimuli. The external heating reduces the spontaneous polarization hence the skyrmion motion barrier, while the skyrmions shrink under the electric field, which could weaken the lattice pinning and interactions between the skyrmions. The mechanical force transforms the skyrmions into c-domain in the vicinity of the indenter center under the electric field, providing the space and driving force needed for the skyrmions to move. This study confirmed that the skyrmions are quasi-particles that can move collectively, while also providing concrete guidance for the further design of polar skyrmion-based electronic devices.Comment: 17 pages, 4 figure

SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery

Abstract With the continuing improvement of remote-sensing (RS) sensors, it is crucial to monitor Earth surface changes at fine scale and in great detail. Thus, semantic change detection (SCD), which is capable of locating and identifying "from-to" change information simultaneously, is gaining growing attention in RS community. However, due to the limitation of large-scale SCD datasets, most existing SCD methods are focused on scene-level changes, where semantic change maps are generated with only coarse boundary or scarce category information. To address this issue, we propose a novel convolutional network for large-scale SCD (SCDNet). It is based on a Siamese UNet architecture, which consists of two encoders and two decoders with shared weights. First, multi-temporal images are given as input to the encoders to extract multi-scale deep representations. A multi-scale atrous convolution (MAC) unit is inserted at the end of the encoders to enlarge the receptive field as well as capturing multi-scale information. Then, difference feature maps are generated for each scale, which are combined with feature maps from the encoders to serve as inputs for the decoders. Attention mechanism and deep supervision strategy are further introduced to improve network performance. Finally, we utilize softmax layer to produce a semantic change map for each time image. Extensive experiments are carried out on two large-scale high-resolution SCD datasets, which demonstrates the effectiveness and superiority of the proposed method