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The matrix-multiply assist (MMA) facility is the latest addition to IBM's power instruction set architecture and first shipped in the recently introduced POWER10 processor. MMA is designed to accelerate matrix–matrix operations, such as matrix multiplication and convolution, using instructions that compute the outer product of vector-register operands. Outer product computations have been used for decades in linear algebra libraries to deliver high-performance implementations of matrix operations. Such libraries use conventional single-instruction–multiple-data (SIMD) instructions to emulate outer product operations. MMA in POWER10 is the first hardware with direct support for outer product operations released in the market. MMA operates with the widest diversity of data types compared to any accelerator design currently announced. Unleashing the high-performance enabled by MMA requires careful code generation. Two key considerations for optimal MMA code performance are 1) the choice of accumulation layout when maximizing the using the accumulators and 2) the selection of matrix access order. This article shows that over 92% of peak performance in POWER10 with MMA can be achieved when the code generation makes the right choices. [ABSTRACT FROM AUTHOR]

A high-gradient magnetic separator that utilizes two kinds of plate including flat and lattice plates to provide a high-gradient magnetic field and separate magnetic particles from suspension was investigated in this paper. The separation of magnetic particles by in-situ observation has been carried out, and the results indicated that the magnetic particles formed shapes like a spike at the entrance of the separation system where magnetic particles were influenced by the magnetic field. Although long spike shapes were also observed at lower flow rates and lower magnetic field, the spikes appeared to be denser under the conditions of high magnetic field and high flow rates. The results achieved from in-situ observations can provide valuable information for the simulation of such a system. The two most important experimental variables, flow rate and distance between magnets, have been optimized for the separation of magnetic particles from suspension. The most efficient backwash system was also offered to reuse magnetic particles. The suggested system can be used to separate magnetic particles larger than 50 μm where using filter matrix is limited due to fast clogging. [ABSTRACT FROM AUTHOR]

The YBCO Roebel cable is a promising option for the high-energy accelerator magnets and tokamak devices with high-current carrying capability and compactness. Referring to the complex current sharing and normal zone propagation, it is difficult to apply the traditional superconductor quench measurement methodology to such a long Roebel cable (2 m) in an adiabatic condition. An alternative method for the quench measurement is to make a Roebel pancake coil cowound with ribbon fiberglass. This paper starts reporting the methodology to wind the coil with impregnation process and the wire installment for the quenchmeasurement. Some initial performancemeasurements and a first trial of the quenchmeasurement were processed in LN2 at 77 K to show the robust performance of this Roebel cable at all the current contacts while maintaining the cable effectively adiabatic or similar to real magnets. [ABSTRACT FROM AUTHOR]

This report is on the design, control strategy, implementation, and performance evaluation of a novel leg–wheel transformable robot called TurboQuad, which can perform fast gait/mode coordination and transitions in wheeled mode, in legged trotting, and in legged walking while in motion. This functionality was achieved by including two novel setups in the robot that were not included in its predecessor, Quattroped. First, a new leg–wheel mechanism was used, in which the leg/wheel operation and its in situ transition can be driven by the same set of motors, so the actuation system and power can be utilized efficiently. Second, a bio-inspired control strategy was applied based on the central pattern generator and coupled oscillator networks, in which the gait/mode generation, coordination, and transitions can be integrally controlled. The robot was empirically built and its performances in the described three gaits/modes as well as the transitions among them were experimentally evaluated and will be discussed in this paper. [ABSTRACT FROM AUTHOR]

In order to guarantee data reliability, erasure codes have been used in distributed storage systems. Nevertheless, this mechanism suffers from the repair problem that excess data are needed to repair a single failure, causing both high bandwidth consuming for the network and heavy computing load on the replacement node. To reduce repair traffic, researchers pointed out the tradeoff between storage and repair traffic and proposed regenerating codes by combining network coding. However, the combination only focuses on the storage terminal and the construction of the codes is quite complicated. Therefore, this paper further combines network coding with network structure and proposes a repair tree model based on general erasure codes to simplify the repair procedure. By decomposing repair computing and distributing it among the tree nodes, our model can mitigate the computing tension. The performance of repair tree is analyzed and evaluated by preliminary emulation. The result shows it can make about three times faster computing than conventional measure and the repair throughput is doubled if there are network bottlenecks. For proper topology, it can significantly reduce the repair traffic. We present algorithms to generate trees across the network topology. At last, we present the idea of extending repair tree to repair multiple failures. [ABSTRACT FROM AUTHOR]

Interactive mobile applications attract lots of attentions recently. They utilize complex algorithms (e.g., machine learning) to provide advanced functions (e.g., object recognition), thus lead to long response time while running on mobile devices. To reduce the response time, researchers propose offloading some compute-intensive parts of mobile applications onto cloud. Existing works aim to optimize general performance (e.g., response time), but ignore the enhancement of application quality (e.g., recognition accuracy), which is also critical to user experience. In this paper, we develop AppBooster, a mobile cloud platform which boosts both general performance and application quality for interactive mobile applications. AppBooster jointly leverages the quality adaptation, computation offloading and parallel speedup to boost the comprehensive performance, which is defined by developers based on the metrics of application quality and general performance. Through combining history-based platform-learned knowledge, developer-provided information and the platform-monitored environment conditions (e.g., workload, network), AppBooster manages applications with optimal computation partitioning scheme and tunable parameter setting thus obtain high comprehensive performance. We evaluate AppBooster with an object recognition application in various network conditions and show AppBooster can significantly boost application performance and obtain 1.3 to 3.5 times better performance than existing strategies. [ABSTRACT FROM AUTHOR]

This paper proposes a highly scalable sensor design for late transition detection in FPGA based platforms. Transition delays occur because of aging mechanisms such as Biased Temperature Instability (BTI) and Hot Carrier Injection (HCI). We propose a sensor clock (SCLK) that is a function of minimum slack time of a set of paths selected for age monitoring. There will be one such clock for many sensors as are needed in an entire FPGA. Our proposed sensor architecture makes it possible for a single SCLK to be shared by all sensors. Additionally, the proposed sensor occupies one slice (basic FPGA logic block), which leads to low area, power, and performance overhead. Using Artix-7-based board, experimental results demonstrate that the proposed aging sensor detects aging earlier than existing sensors and provides less power and performance overheads. [ABSTRACT FROM AUTHOR]

The pay-as-you-go service model impels cloud customers to reduce the usage cost of bandwidth. Traffic Redundancy Elimination (TRE) has been shown to be an effective solution for reducing bandwidth costs, and thus has recently captured significant attention in the cloud environment. By studying the TRE techniques in a trace driven approach, we found that both short-term (time span of seconds) and long-term (time span of hours or days) data redundancy can concurrently appear in the traffic, and solely using either sender-based TRE or receiver-based TRE cannot simultaneously capture both types of traffic redundancy. Also, the efficiency of existing receiver-based TRE solution is susceptible to the data changes compared to the historical data in the cache. In this paper, we propose a Cooperative end-to-end TRE solution (CoRE) that can detect and remove both short-term and long-term redundancy through a two-layer TRE design with cooperative operations between layers. An adaptive prediction algorithm is further proposed to improve TRE efficiency through dynamically adjusting the prediction window size based on the hit ratio of historical predictions. Besides, we enhance CoRE to adapt to different traffic redundancy characteristics of cloud applications to improve its operation cost. Extensive evaluation with several real traces show that CoRE is capable of effectively identifying both short-term and long-term redundancy with low additional cost while ensuring TRE efficiency from data changes. [ABSTRACT FROM PUBLISHER]

The bipartite graph is a ubiquitous data structure that can model the relationship between two entity types: for instance, users and items, queries and webpages. In this paper, we study the problem of ranking vertices of a bipartite graph, based on the graph's link structure as well as prior information about vertices (which we term a query vector). We present a new solution, BiRank, which iteratively assigns scores to vertices and finally converges to a unique stationary ranking. In contrast to the traditional random walk-based methods, BiRank iterates towards optimizing a regularization function, which smooths the graph under the guidance of the query vector. Importantly, we establish how BiRank relates to the Bayesian methodology, enabling the future extension in a probabilistic way. To show the rationale and extendability of the ranking methodology, we further extend it to rank for the more generic $n$ -partite graphs. BiRank's generic modeling of both the graph structure and vertex features enables it to model various ranking hypotheses flexibly. To illustrate its functionality, we apply the BiRank and TriRank (ranking for tripartite graphs) algorithms to two real-world applications: a general ranking scenario that predicts the future popularity of items, and a personalized ranking scenario that recommends items of interest to users. Extensive experiments on both synthetic and real-world datasets demonstrate BiRank's soundness (fast convergence), efficiency (linear in the number of graph edges), and effectiveness (achieving state-of-the-art in the two real-world tasks). [ABSTRACT FROM AUTHOR]

In this paper, we propose ClickSmart, a viewpoint recommendation system that can assist a user in capturing high-quality photographs at well-known tourist locations. ClickSmart can provide real-time viewpoint recommendation based on the preview on the user’s camera, current time, and user’s geolocation. It makes use of publicly available geotagged images along with the associated metadata for learning a recommendation model. We define view-cells, macroblocks in geospace, and propose the concepts of popularity, quality, and uniqueness of view-cells from the viewpoint perspective. Viewpoint recommendation is generated at the granularity of a view-cell and is based on its popularity, quality, and uniqueness, which are estimated using social media cues associated with images. We further observe that contextual information such as time and weather conditions play an important role in photography, and therefore augment the recommendation system with the associated context. ClickSmart also takes into account the presence of people in the view for making the recommendation. It can provide two kinds of recommendations, quality based and uniqueness based. Although both were found effective in the experimental evaluation, our user study showed that uniqueness-based recommendation was preferred more by skilled photographers compared with amateurs. [ABSTRACT FROM PUBLISHER]

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers. [ABSTRACT FROM PUBLISHER]

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