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

WolfPath : accelerating iterative traversing-based graph processing algorithms on GPU

There is the significant interest nowadays in developing the frameworks of parallelizing the processing for the large graphs such as social networks, Web graphs, etc. Most parallel graph processing frameworks employ iterative processing model. However, by benchmarking the state-of-art GPU-based graph processing frameworks, we observed that the performance of iterative traversing-based graph algorithms (such as Bread First Search, Single Source Shortest Path and so on) on GPU is limited by the frequent data exchange between host and GPU. In order to tackle the problem, we develop a GPU-based graph framework called WolfPath to accelerate the processing of iterative traversing-based graph processing algorithms. In WolfPath, the iterative process is guided by the graph diameter to eliminate the frequent data exchange between host and GPU. To accomplish this goal, WolfPath proposes a data structure called Layered Edge list to represent the graph, from which the graph diameter is known before the start of graph processing. In order to enhance the applicability of our WolfPath framework, a graph preprocessing algorithm is also developed in this work to convert any graph into the format of the Layered Edge list. We conducted extensive experiments to verify the effectiveness of WolfPath. The experimental results show that WolfPath achieves significant speedup over the state-of-art GPU-based in-memory and out-of-memory graph processing frameworks

Inflammasomes and the Maintenance of Hematopoietic Homeostasis: New Perspectives and Opportunities

Hematopoietic stem cells (HSCs) regularly produce various blood cells throughout life via their self-renewal, proliferation, and differentiation abilities. Most HSCs remain quiescent in the bone marrow (BM) and respond in a timely manner to either physiological or pathological cues, but the underlying mechanisms remain to be further elucidated. In the past few years, accumulating evidence has highlighted an intermediate role of inflammasome activation in hematopoietic maintenance, post-hematopoietic transplantation complications, and senescence. As a cytosolic protein complex, the inflammasome participates in immune responses by generating a caspase cascade and inducing cytokine secretion. This process is generally triggered by signals from purinergic receptors that integrate extracellular stimuli such as the metabolic factor ATP via P2 receptors. Furthermore, targeted modulation/inhibition of specific inflammasomes may help to maintain/restore adequate hematopoietic homeostasis. In this review, we will first summarize the possible relationships between inflammasome activation and homeostasis based on certain interesting phenomena. The cellular and molecular mechanism by which purinergic receptors integrate extracellular cues to activate inflammasomes inside HSCs will then be described. We will also discuss the therapeutic potential of targeting inflammasomes and their components in some diseases through pharmacological or genetic strategies

Inflammasomes and the Maintenance of Hematopoietic Homeostasis: New Perspectives and Opportunities

Hematopoietic stem cells (HSCs) regularly produce various blood cells throughout life via their self-renewal, proliferation, and differentiation abilities. Most HSCs remain quiescent in the bone marrow (BM) and respond in a timely manner to either physiological or pathological cues, but the underlying mechanisms remain to be further elucidated. In the past few years, accumulating evidence has highlighted an intermediate role of inflammasome activation in hematopoietic maintenance, post-hematopoietic transplantation complications, and senescence. As a cytosolic protein complex, the inflammasome participates in immune responses by generating a caspase cascade and inducing cytokine secretion. This process is generally triggered by signals from purinergic receptors that integrate extracellular stimuli such as the metabolic factor ATP via P2 receptors. Furthermore, targeted modulation/inhibition of specific inflammasomes may help to maintain/restore adequate hematopoietic homeostasis. In this review, we will first summarize the possible relationships between inflammasome activation and homeostasis based on certain interesting phenomena. The cellular and molecular mechanism by which purinergic receptors integrate extracellular cues to activate inflammasomes inside HSCs will then be described. We will also discuss the therapeutic potential of targeting inflammasomes and their components in some diseases through pharmacological or genetic strategies

Research on the settlement of levee and the formation mechanism of pavement crack in heightening and thickening levee engineering

In view of the heightening and thickening levee project which already built, the settlement and deformation on the top of the landside slope of levee and the cracks on the levee were investigated. The finite element method was used to simulated the settlement of the levee. The results show that after heightening and thickening, the thickness of the new filled soil is uneven in the cross-sectional direction of the levee, resulting in a significant difference in the distribution of additional stress and settlement. In addition, the top of the original levee has smaller deformation, while the newly filled levee has larger deformation, which causes uneven settlement on the top of the embankment. The presented results have guiding significance for the settlement and deformation prevention of levee project

A Research on Cross-Regional Debris Flow Susceptibility Mapping Based on Transfer Learning

Debris flow susceptibility mapping (DFSM), which has proven to be one of the most effective tools for risk management, faces a variety of problems. To realize the rational use of debris flow sample resources and improve the modeling efficiency, a unified model based on transfer learning was established for cross-regional DFSM. First, samples with 10 features collected from two debris flow-prone areas were separately used to perform factor prediction ability analysis (FPAA) based on the information gain ratio (IGR) method and then develop traditional machine learning models based on random forests (RF). Secondly, two feature matrices representing different areas were projected into a common latent feature space to obtain two new feature matrices. Then, the samples with new features were used together for FPAA and developing a unified machine learning model. Finally, the performance of the models was obtained and compared based on the area under curves (AUC) and some statistical results. All the conditioning factors played different roles in debris flow prediction in the two study areas, based on which two traditional models and a unified model were established. The unified model based on feature transferring realized efficient cross-regional modeling, solved the unconvincing problem of limited sample modeling, and enabled more accurate identification of some debris flow samples

A Research on Cross-Regional Debris Flow Susceptibility Mapping Based on Transfer Learning

Debris flow susceptibility mapping (DFSM), which has proven to be one of the most effective tools for risk management, faces a variety of problems. To realize the rational use of debris flow sample resources and improve the modeling efficiency, a unified model based on transfer learning was established for cross-regional DFSM. First, samples with 10 features collected from two debris flow-prone areas were separately used to perform factor prediction ability analysis (FPAA) based on the information gain ratio (IGR) method and then develop traditional machine learning models based on random forests (RF). Secondly, two feature matrices representing different areas were projected into a common latent feature space to obtain two new feature matrices. Then, the samples with new features were used together for FPAA and developing a unified machine learning model. Finally, the performance of the models was obtained and compared based on the area under curves (AUC) and some statistical results. All the conditioning factors played different roles in debris flow prediction in the two study areas, based on which two traditional models and a unified model were established. The unified model based on feature transferring realized efficient cross-regional modeling, solved the unconvincing problem of limited sample modeling, and enabled more accurate identification of some debris flow samples