Preparation of Al2O3/Ti(C,N)/ZrO2/CaF2@Al(OH)3 Ceramic Tools and Cutting Performance in Turning.

Aiming at the contradiction between the lubricating performance and mechanical performance of self-lubricating ceramic tools. CaF2@Al(OH)3 particles were prepared by the heterogeneous nucleation method. An Al2O3/Ti(C,N) ceramic tool with CaF2@Al2(OH)3 particles and ZrO2 whiskers was prepared by hot press sintering (frittage). The cutting performances and wear mechanisms of this ceramic tool were investigated. Compared with the Al2O3/Ti(C,N) ceramic tool, the Al2O3/Ti(C,N)/ZrO2/CaF2@Al(OH)3 ceramic tool had lower cutting temperatures and surface roughness. When the cutting speed was increased from 100m/min to 300m/min, a lot of CaF2 was smeared onto the surface of the ceramic tool, and the flank wear of the Al2O3/Ti(C,N)/ZrO2/CaF2@Al(OH)3 ceramic tool was reduced. The main wear mechanisms of the Al2O3/Ti(C,N)/ZrO2/CaF2@Al(OH)3 ceramic tool were adhesive wear and micro-chipping. The formation of solid lubricating film and the improvement of fracture toughness by adding ZrO2 whiskers and CaF2@Al(OH)3 were important factors for the Al2O3/Ti(C,N)/ZrO2/CaF2@Al(OH)3 ceramic tool to have better cutting performances.

Efficient computation of motif discovery on Intel Many Integrated Core (MIC) Architecture.

BACKGROUND: Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms. RESULTS: In this paper, we accelerate MEME algorithm targeted on Intel Many Integrated Core (MIC) Architecture and present a parallel implementation of MEME called MIC-MEME base on hybrid CPU/MIC computing framework. Our method focuses on parallelizing the starting point searching method and improving iteration updating strategy of the algorithm. MIC-MEME has achieved significant speedups of 26.6 for ZOOPS model and 30.2 for OOPS model on average for the overall runtime when benchmarked on the experimental platform with two Xeon Phi 3120 coprocessors. CONCLUSIONS: Furthermore, MIC-MEME has been compared with state-of-arts methods and it shows good scalability with respect to dataset size and the number of MICs. Source code: https://github.com/hkwkevin28/MIC-MEME .

Bioinformatics applications on Apache Spark.

With the rapid development of next-generation sequencing technology, ever-increasing quantities of genomic data pose a tremendous challenge to data processing. Therefore, there is an urgent need for highly scalable and powerful computational systems. Among the state-of-the-art parallel computing platforms, Apache Spark is a fast, general-purpose, in-memory, iterative computing framework for large-scale data processing that ensures high fault tolerance and high scalability by introducing the resilient distributed dataset abstraction. In terms of performance, Spark can be up to 100 times faster in terms of memory access and 10 times faster in terms of disk access than Hadoop. Moreover, it provides advanced application programming interfaces in Java, Scala, Python, and R. It also supports some advanced components, including Spark SQL for structured data processing, MLlib for machine learning, GraphX for computing graphs, and Spark Streaming for stream computing. We surveyed Spark-based applications used in next-generation sequencing and other biological domains, such as epigenetics, phylogeny, and drug discovery. The results of this survey are used to provide a comprehensive guideline allowing bioinformatics researchers to apply Spark in their own fields.

cmFSM: a scalable CPU-MIC coordinated drug-finding tool by frequent subgraph mining.

BACKGROUND: Frequent subgraphs mining is a significant problem in many practical domains. The solution of this kind of problem can particularly used in some large-scale drug molecular or biological libraries to help us find drugs or core biological structures rapidly and predict toxicity of some unknown compounds. The main challenge is its efficiency, as (i) it is computationally intensive to test for graph isomorphisms, and (ii) the graph collection to be mined and mining results can be very large. Existing solutions often require days to derive mining results from biological networks even with relative low support threshold. Also, the whole mining results always cannot be stored in single node memory. RESULTS: In this paper, we implement a parallel acceleration tool for classical frequent subgraph mining algorithm called cmFSM. The core idea is to employ parallel techniques to parallelize extension tasks, so as to reduce computation time. On the other hand, we employ multi-node strategy to solve the problem of memory constraints. The parallel optimization of cmFSM is carried out on three different levels, including the fine-grained OpenMP parallelization on single node, multi-node multi-process parallel acceleration and CPU-MIC collaborated parallel optimization. CONCLUSIONS: Evaluation results show that cmFSM clearly outperforms the existing state-of-the-art miners even if we only hold a few parallel computing resources. It means that cmFSM provides a practical solution to frequent subgraph mining problem with huge number of mining results. Specifically, our solution is up to one order of magnitude faster than the best CPU-based approach on single node and presents a promising scalability of massive mining tasks in multi-node scenario. More source code are available at:Source Code: https://github.com/ysycloud/cmFSM .