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When the under-actuated hand pinches the object on the worktable, the trajectory of the end of the finger is not a straight line, which makes it difficult for the hand to grasp the object from its both sides. In order to overcome ...
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When the under-actuated hand pinches the object on the worktable, the trajectory of the end of the finger is not a straight line, which makes it difficult for the hand to grasp the object from its both sides. In order to overcome this shortcoming, this paper proposes a new configuration of the linear-parallel and self-adaptive under-actuated hand which uses the four-link and sliding base mechanism to compensate for the vertical displacement of the end of the finger. Based on this new configuration, the mechanical structure of the under-actuated hand is designed, which has five degrees of freedom (DOFs), and is mainly composed of two fingers, a sliding base, four link compensation mechanisms and an outer base. These two fingers have exactly the same structure and size, where each finger uses only one motor to control two joints of the finger which realizes the under-actuated function. Through the cooperation of the four-link and sliding base mechanism, the under-actuated hand can realize the linear-parallel and self-adaptive hybrid grasping mode. Kinematics analysis and contact force analysis of the under-actuated hand are discussed, and the prototype of the under-actuated hand is developed to carry out the grasping experiments. The results of the simulation and experiment all show that the under-actuated hand has good motion performance and grasping stability and can be used as an end effector for intelligent robots.
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This paper introduces a novel underactuated hand, the PASA-GB hand, which has a hybrid grasping mode. The hybrid grasping mode is a combination of parallel pinching (PA) grasp and self-adaptive enveloping (SA) grasp. In order to e...
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This paper introduces a novel underactuated hand, the PASA-GB hand, which has a hybrid grasping mode. The hybrid grasping mode is a combination of parallel pinching (PA) grasp and self-adaptive enveloping (SA) grasp. In order to estimate the performance of grasping objects, the potential energy method is used to analyze the grasping poses and stabilities of the PASA-GB hand. The calculation of force distribution shows the influence of the size and position of objects and provides a method to optimize the force distribution. The switch condition between pinching and enveloping grasp is analyzed in detail. Experimental results verify wide adaptability and high practicability of the PASA-GB hand.
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In this paper, we present a post-silicon self-correction technique to leverage the redundancy present in parallel prefix adders (PPA). Our technique is based on the fact that a set of carries in PPAs can be made mutually exclusive...
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In this paper, we present a post-silicon self-correction technique to leverage the redundancy present in parallel prefix adders (PPA). Our technique is based on the fact that a set of carries in PPAs can be made mutually exclusive. Therefore, defects in a set of bits can only corrupt the corresponding set of Sum outputs whereas the remaining Sums are computed correctly. To efficiently utilize the above property of PPAs in presence of defects, we perform addition in multiple clock cycles. In cycle-1, one of the correct set of bits are computed and stored at the output registers. In the subsequent cycles, the operands are shifted by one bit at a time and the remaining sets of bits are recovered. This allows us to compute the correct output at the cost of throughput degradation and minor area and delay overhead while maintaining high frequency and yield. Finally, the proposed technique is used in a superscalar processor, whereby the self-correcting adder is assigned lower priority than fault-free adders to reduce the overall throughput degradation.
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This article describes the context, design, and recent development of the LAPACK for clusters (LFC) project. It has been developed in the framework of Self-Adapting Numerical Software (SANS) since we believe such an approach can d...
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This article describes the context, design, and recent development of the LAPACK for clusters (LFC) project. It has been developed in the framework of Self-Adapting Numerical Software (SANS) since we believe such an approach can deliver the convenience and ease of use of existing sequential environments bundled with the power and versatility of highly tuned parallel codes that execute on clusters. Accomplishing this task is far from trivial as we argue in the paper by presenting pertinent case studies and possible usage scenarios.
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Flash memory based solid state drives (SSDs) have emerged as a new alternative to replace magnetic disks due to their high performance and low power consumption. However, random writes on SSDs are much slower than SSD reads. There...
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Flash memory based solid state drives (SSDs) have emerged as a new alternative to replace magnetic disks due to their high performance and low power consumption. However, random writes on SSDs are much slower than SSD reads. Therefore, traditional index structures, which are designed based on the symmetrical I/O property of magnetic disks, cannot completely exert the high performance of SSDs. In this paper, we propose an SSD-optimized linear hashing index called Self-Adaptive Linear Hashing (SAL-hashing) to reduce small random-writes to SSDs that are caused by index operations. The contributions of our work are manifold. First, we propose to organize the buckets of a linear hashing index into groups and sets to facilitate coarse-grained writes and adaptivity to access patterns. A group consisting of a fixed number of buckets is proposed to transform small random writes to buckets into coarse-grained writes and in turn improve write performance of the index. A set consists of a number of groups, and we propose to employ different split strategies for each set. With this mechanism, SAL-hashing is able to adapt to the changes of access patterns. Second, we attach a log region to each set, and amortize the cost of reads and writes by committing updates to the log region in batch. Third, in order to reduce search cost, each log region is equipped with Bloom filters to index update logs. We devise a cost-based online algorithm to adaptively merge the log region with the corresponding set when the set becomes search-intensive. Fourth, we propose a new technique called virtual split to optimize the search performance of SAL-hashing. Finally, we propose a new scheme for the management of the log buffer. We conduct extensive experiments on real SSDs. The results suggest that our proposal is self-adaptive according to the change of access patterns, and outperforms several competitors under various workloads.
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Stream processing applications are common computing workloads that demand parallelism to increase their performance. As in the past, parallel programming remains a difficult task for application programmers. The complexity increas...
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Stream processing applications are common computing workloads that demand parallelism to increase their performance. As in the past, parallel programming remains a difficult task for application programmers. The complexity increases when application programmers must set nonintuitive parallelism parameters, that is, the degree of parallelism. The main problem is that state-of-the-art libraries use a static degree of parallelism and are not sufficiently abstracted for developing stream processing applications. In this article, we propose a self-adaptive regulation of the degree of parallelism to provide higher-level abstractions. Flexibility is provided to programmers with two new self-adaptive strategies, one is for performance experts, and the other abstracts the need to set a performance goal. We evaluated our solution using compiler transformation rules to generate parallel code with the SPar domain-specific language. The experimental results with real-world applications highlighted higher abstraction levels without significant performance degradation in comparison to static executions. The strategy for performance experts achieved slightly higher performance than the one that works without user-defined performance goals.
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In this paper we develop a self-adaptive projection and contraction method for the linear complementarity problem (LCP). This method improves the practical performance of the modified projection and contraction method in [10] by a...
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In this paper we develop a self-adaptive projection and contraction method for the linear complementarity problem (LCP). This method improves the practical performance of the modified projection and contraction method in [10] by adopting a self-adaptive technique. The global convergence of our new method is proved under mild assumptions. Our numerical tests clearly demonstrate the necessity and effectiveness of our proposed method.
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In this paper we suggest a new approach for solving the hyperplane problem, also known as "wavefront" computation. In direct contrast to most approaches that reduce the problem to an integer programming one or use several heuristi...
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In this paper we suggest a new approach for solving the hyperplane problem, also known as "wavefront" computation. In direct contrast to most approaches that reduce the problem to an integer programming one or use several heuristic approaches, we gather information at compile time and delegate the solution to run time. We present an adaptive technique which intuitively calculates which new threads will be able to be executed in the next computation cycle based on which threads are executed in the current one. Moving the solution to the run time environment provides us with higher versatility alongside a perfect solution of the underlying hyperplane pattern being discovered without the need to perform any prior calculations. The main contribution of this paper is the presentation of the self adaptive algorithm, an algorithm which does not need to know the tile size (which controls the granularity of parallelism) beforehand. Instead, the algorithm itself adapts the tile size while the program is running in order to achieve optimal efficiency. Experimental results show that if we have a sufficient number of parallel processing elements to diffuse the scheduler's workload, its overhead becomes low enough that it is overshadowed by the net gain in parallelism. For the implementation of the algorithm we suggest, and for our experimentations our parallelizing compiler C2uTC/SL is used, a C parallelizing compiler which maps sequential programs on the SVP processor and model.
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Self-Organizing Neural Networks (SONNs) have a wide range of applications with massive computational requirements that often need to be satisfied with optimized parallel algorithms and implementations. In literature, SONN have bee...
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Self-Organizing Neural Networks (SONNs) have a wide range of applications with massive computational requirements that often need to be satisfied with optimized parallel algorithms and implementations. In literature, SONN have been generally parallelized with GPU computing according to a single-signal paradigm: each GPU thread manages one or more nodes of the network and works concurrently on one input signal at the time. This paper presents two contributions. The first one is the experimental proof that the single-signal approach for SONNs is not optimal for the task, as it is intrinsically sequential at its core and thus inherently limited in its performance. The non-optimality of the single-signal paradigm is illustrated via a specific and simplified benchmark. The second contribution is the introduction of a new multi-signal paradigm for the parallelization of SONNs, whereby multiple signals are processed at once in each iteration hence allowing different GPU threads to work on different signals. The advantages of the multi-signal approach are shown through several benchmarks involving the Self-Organizing Adaptive Map (SOAM) algorithm as a basis for evaluation. Having a graph-based termination condition that depends on the features of the network being grown, the SOAM algorithm allows assessing both functional equivalence and performances of the paradigm proposed without relying on arbitrary thresholds. Nonetheless, the evaluation proposed has a broader scope since it refers to a unified framework for the GPU parallelization of a generic SONN. (C) 2019 Elsevier Ltd. All rights reserved.
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Several real-world parallel applications are becoming more dynamic and long-running, demanding online (at run-time) adaptations. Stream processing is a representative scenario that computes data items arriving in real-time and whe...
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Several real-world parallel applications are becoming more dynamic and long-running, demanding online (at run-time) adaptations. Stream processing is a representative scenario that computes data items arriving in real-time and where parallel executions are necessary. However, it is challenging for humans to monitor and manually self-optimize complex and long-running parallel executions continuously. Moreover, although high-level and structured parallel programming aims to facilitate parallelism, several issues still need to be addressed for improving the existing abstractions. In this paper, we extend self-adaptiveness for supporting autonomous and online changes of the parallel pattern compositions. Online self-adaptation is achieved with an online profiler that characterizes the applications, which is combined with a new self-adaptive strategy and a model for smooth transitions on reconfigurations. The solution provides a new abstraction layer that enables application programmers to define non-functional requirements instead of hand-tuning complex configurations. Hence, we contribute with additional abstractions and flexible self-adaptation for responsiveness at runtime. The proposed solution is evaluated with applications having different processing characteristics, workloads, and configurations. The results show that it is possible to provide additional abstractions, flexibility, and responsiveness while achieving performance comparable to the best static configuration executions.
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