1603873
Application-Transparent Checkpoint/Restart for MPI Programs over InfiniBand
Ultra-scale computer clusters with high speed interconnects, such as InfiniBand, are being widely deployed for their excellent performance and cost effectiveness. However, the failure rate on these clusters also increases along with their augmented number of components. Thus, it becomes critical for such systems to be equipped with fault tolerance support. In this paper, we present our design and implementation of checkpoint/restart framework for MPI programs running over InfiniBand clusters. Our design enables low-overhead, application-transparent checkpointing. It uses coordinated protocol to save the current state of the whole MPI job to reliable storage, which allows users to perform rollback recovery if the system runs into faulty states later. Our solution has been incorporated into MVAPICH2, an open-source high performance MPI-2 implementation over InfiniBand. Performance evaluation of this implementation has been carried out using NAS benchmarks, HPL benchmark, and a real-world application called GROMACS. Experimental results indicate that in our design, the overhead to take checkpoints is low, and the performance impact for checkpointing applications periodically is insignificant. For example, time for checkpointing GROMACS is less than 0.3% of the execution time, and its performance only decreases by 4% with checkpoints taken every minute. To the best of our knowledge, this work is the first report of checkpoint/restart support for MPI over InfiniBand clusters in the literature
7089503
Supporting MPI-2 One Sided Communication on Multi-rail InfiniBand Clusters: Design Challenges and Performance Benefits
In cluster computing, InfiniBand has emerged as a popular high performance interconnect with MPI as the de facto programming model. However, even with InfiniBand, bandwidth can become a bottleneck for clusters executing communication intensive applications. Multi-rail cluster configurations with MPI-1 are being proposed to alleviate this problem. Recently, MPI-2 with support for one-sided communication is gaining significance. In this paper, we take the challenge of designing high performance MPI-2 one-sided communication on multi-rail InfiniBand clusters. We propose a unified MPI-2 design for different configurations of multi-rail networks (multiple ports, multiple HCAs and combinations). We present various issues associated with one-sided communication such as multiple synchronization messages, scheduling of RDMA (Read, Write) operations, ordering relaxation and discuss their implications on our design. Our performance results show that multi-rail networks can significantly improve MPI-2 one-sided communication performance. Using PCI-Express with two-ports, we can achieve a peak MPI_Put bidirectional bandwidth of 2620 Million Bytes/s, compared to 1910 MB/s for single-rail implementation. For PCI-X with two HCAs, we can almost double the throughput and reduce the latency to half for large messages.
7610617
High Performance VMM-Bypass I/O in Virtual Machines
Currently, I/O device virtualization models in virtual machine (VM) environments require involvement of a virtual machine monitor (VMM) and/or a privileged VM for each I/O operation, which may turn out to be a performance bottleneck for systems with high I/O demands, especially those equipped with modern high speed interconnects such as InfiniBand. 
 
In this paper, we propose a new device virtualization model called VMM-bypass I/O, which extends the idea of OS-bypass originated from user-level communication. Essentially, VMM-bypass allows time-critical I/O operations to be carried out directly in guest VMs without involvement of the VMM and/or a privileged VM. By exploiting the intelligence found in modern high speed network interfaces, VMM-bypass can significantly improve I/O and communication performance for VMs without sacrificing safety or isolation. 
 
To demonstrate the idea of VMM-bypass, we have developed a prototype called Xen-IB, which offers InfiniBand virtualization support in the Xen 3.0 VM environment. Xen-IB runs with current InfiniBand hardware and does not require modifications to existing user-level applications or kernel-level drivers that use InfiniBand. Our performance measurements show that Xen-IB is able to achieve nearly the same raw performance as the original InfiniBand driver running in a non-virtualized environment.
2376785
Designing Efficient Asynchronous Memory Operations Using Hardware Copy Engine: A Case Study with I/OAT
Memory copies for bulk data transport incur large overheads due to CPU stalling, small register-size data movement, etc. Intel's I/O Acceleration Technology offers an asynchronous memory copy engine in kernel space which alleviates such overheads. In this paper, we propose a set of designs for asynchronous memory operations in user space for both single process (as an offloaded memcpy()) and lPC using the copy engine. We analyze our design based on overlap efficiency, performance and cache utilization. Our microbenchmark results show that using the copy engine for performing memory copies can achieve close to 87% overlap with computation. Further, the copy engine improves the copy latency of bulk memory data transfers by 50% and avoids cache pollution effects. With the emergence of multi-core architectures, the support for asynchronous memory operations holds a lot of promise in reducing the gap between the memory and processor performance.
62635072
Kernel autoassociator with applications to visual classification
Autoassociator is an important issue in concept learning, and the learned concept of a particular class can be used to distinguish the class from the others. For nonlinear autoassociation, This work presents a new model referred to as kernel autoassociator. Using kernel feature space as a potential nonlinear manifold, the model formulates the autoassociation as a special reconstruction problem from kernel feature space to input space. Two methods are developed to solve the problem. We evaluate the autoassociator with artificial data, and apply it to handwritten digit recognition and multiview face recognition, yielding positive experimental results.
13283914
ConKMeL: a contextual knowledge management framework to support multimedia e-Learning
As the latest stage of learning and training evolution, e-Learning is supposed to provide intelligent functionalities not only in processing multi-media education resources but also in supporting context-sensitive pedagogical education processes. Towards providing an integrated solution for intelligent multimedia e-Learning, this paper presents a context-aware knowledge management framework named ConKMeL. Proposed framework features a semantic context-based approach for representing and integrating information and knowledge in e-Learning. Requirement analysis in university e-Learning environments shows that knowledge communications are usually in a hybrid mode across different conceptual levels. Based on the fact, a multi-layer contextual knowledge representation model called KG (knowledge graph) is presented. Corresponding key issues in development such as context-based knowledge retrieval and logical knowledge interpretation are discussed. On the application side, a scenario-based learning case study is shown to demonstrate the concepts and techniques developed in the ConKMeL framework.
2799741
Towards integrating semantics of multi-media resources and processes in e-Learning
Internet-based e-Learning has experienced a boom and bust situation in the past 10 years [32]. To bring in new forces to knowledge-oriented e-Learning, this paper addresses the semantic integration issue of multi-media resources and learning processes with theoretical learning supports in an integrated framework. This paper proposes a context-mediated approach that aims to enable semantic-based inter-operations across knowledge domains, even across the WWW and the Semantic Web [8]. The proposed semantic e-Learning framework enables intelligent operations of heterogeneous multi-media contents based on a generic semantic context intermediation model. This framework supports intelligent e-Learning with a knowledge network for knowledge object visualization, an enhanced Kolb's learning cycle [31] to guide learning practices, and a learning health care framework for personalized learning.
7757717
A kernel autoassociator approach to pattern classification
Autoassociators are a special type of neural networks which, by learning to reproduce a given set of patterns, grasp the underlying concept that is useful for pattern classification. In this paper, we present a novel nonlinear model referred to as kernel autoassociators based on kernel methods. While conventional non-linear autoassociation models emphasize searching for the non-linear representations of input patterns, a kernel autoassociator takes a kernel feature space as the nonlinear manifold, and places emphasis on the reconstruction of input patterns from the kernel feature space. Two methods are proposed to address the reconstruction problem, using linear and multivariate polynomial functions, respectively. We apply the proposed model to novelty detection with or without novelty examples and study it on the promoter detection and sonar target recognition problems. We also apply the model to mclass classification problems including wine recognition, glass recognition, handwritten digit recognition, and face recognition. The experimental results show that, compared with conventional autoassociators and other recognition systems, kernel autoassociators can provide better or comparable performance for concept learning and recognition in various domains.
17604511
POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome
One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.