The Investigation of Some Physical, Physiological and Anthropometric Parameters of Visually Impaired and Non-impaired a National Male Judoka

It was pointed to analyze some physical, physiological and anthropometric parameters of visually impaired and non-impaired A National male judoka in this study. A total of 14 volunteer A National male judoka, of which 8 were visually impaired (age: 25.12 ± 3.75, disability status: 20-200) and 6 were not visually impaired (age: 21.50 ± 1.51), participated in this study. Statistical data was evaluated by using Independent Samples T-test in SPSS package program. There were no considerable difference between impaired and non-impaired judoka with respect to grip strength, vertical jump, 30-s sit-up, 30-s push-up, anaerobic power and auditory reaction test parameters among the physical and physiological tests. No significant difference between impaired and non-impaired judoka was detected with respect to body fat ratio, one of the anthropometric measurements performed. All in all, it was observed in national judoka investigated in this study that impairment variable didn’t significantly affected physical, physiological and anthropometric parameter values (p>0.05). It is considered that vision factor doesn’t have negatory impact on the physical performance grades because of the similarity of the elite judoka’s training levels

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX. We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems. We achieve this by (i) designing the ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers; (iv) adding high-performance core operators while retaining flexibility to support optimizers with fully deterministic updates (v) leveraging reduced precision communications, multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we develop and briefly comment on distributed data ingestion and other supporting services that are required for the robust and efficient end-to-end training in production environments

Routing Algorithms for High-Performance VLSI Packaging

We have seen dramatic advances in the IC technology in the past several years. The shrinkage of die sizes and the increase in functional complexities made the circuits more and more dense. Furthermore, the number of timing critical nets in a typical high-end design has increased considerably due to increasing clock frequencies. These factors have brought significant routing challenges that cannot be handled by traditional board routing algorithms. In this dissertation, we propose novel routing algorithms targeted at handling the challenges due to increasing package densities, and increasing clock frequencies. Routing nets within minimum and maximum length bounds is an important requirement for high-speed VLSI packages. For this problem, we first propose a Lagrangian relaxation based length matching routing algorithm, where the objective of satisfying min-max length constraints is effectively incorporated into the actual routing problem. Our experiments demonstrate that our algorithm outperforms a commonly used ad hoc methodology, especially when the length constraints are tight. Although this algorithm can be used for more general routing problems, we also consider more restricted yet common problem instances, and propose more effective routing algorithms for them. Specifically, we first focus on the problem of two-layer bus routing between component boundaries. We model this problem as a job scheduling problem, and propose algorithms to solve it effectively. After that, we focus on the problem of routing bus structures between component boundaries on a single layer. For this, we propose algorithms that are proven to give close-to-optimal solutions. As the package densities are increasing, routing nets from individual pins within dense components to the component boundaries (escape routing) is becoming the main bottleneck in terms of overall routability. Furthermore, solving the escape routing problem in each component independently is not an effective methodology for high-end board designs, since it ignores the wiring requirements between different components. For this, we propose novel models and algorithms to solve the escape routing problem in multiple components simultaneously, such that the number of crossings in the intermediate area (between components) is minimized. Our experiments demonstrate that these algorithms can reduce via requirements substantially, compared to a net-by-net methodology. We also consider practical generalizations of these models, and discuss how to incorporate several high-speed design constraints into the framework of these algorithms. Finally, we focus on the problem of escape routing within dense pin clusters, which can have arbitrary convex boundaries. We propose a set of sufficient and necessary conditions that guarantee routability outside the escape boundaries. We also discuss how these conditions can be incorporated effectively into an escape routing algorithm

Routing Algorithms for High-Performance VLSI Packaging

152 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.As the package densities are increasing, escape routing is becoming the main bottleneck in terms of overall routability. For this, we propose novel models and algorithms to solve the escape routing problem in multiple components simultaneously, such that the number of crossings in the intermediate area (between components) is minimized. Finally, we focus on the problem of escape routing within dense pin clusters, which can have arbitrary convex boundaries. We propose a set of sufficient and necessary conditions that guarantee routability outside the escape boundaries. We also discuss how these conditions can be incorporated effectively into an escape routing algorithm.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Simultaneous escape routing and layer assignment for dense PCBs

As die sizes are shrinking, and circuit complexities are increasing, the PCB routing problem becomes more and more challenging. Traditional routing algorithms can not handle these challenges effectively, and many high end designs in the industry require manual routing efforts. In this paper we propose a problem decomposition that distinguishes routing within dense components from routing in the intermediate area. In particular, we propose an effective methodology to find the escape routing solution for multiple components simultaneously such that the number of crossings in the intermediate area is minimized. For this, we model the problemasalongest path with forbidden pairs (LPFP) problem, and propose two algorithms for it. The first is an exact polynomialtime algorithm that is guaranteed to find the maximal planar routing solution on one layer. The second is a randomized algorithm that has good scalability characteristics for large circuits. Then we use these algorithms to assign the maximal subset of planar nets to each layer, and then distribute the remaining nets at the end. We demonstrate the effectiveness of these algorithms through experiments on industrial circuits. 1

A provably good algorithm for high performance bus routing

As the clock frequencies used in industrial applications increase, the timing requirements on routing problems become tighter, and current routing tools can not successfully handle these constraints anymore. In this paper, we focus on the high-performance singlelayer bus routing problem, where the objective is to match the lengths of all nets belonging to each bus. An effective approach to solve this problem is to allocate extra routing resources around short nets during routing; and use those resources for length extension afterwards. We first propose a provably optimal algorithm for routing nets with min-area max-length constraints. Then, we extend this algorithm to the case where minimum constraints are given as exact length bounds. We also prove that this algorithm is optimal within a constant factor. Both algorithms proposed are also shown to be scalable for large circuits, since the respective time complexities are O(A) and O(AlogA), where A is the area of the intermediate region between chips. 1

Routing Algorithms for High-Performance VLSI Packaging

152 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005.As the package densities are increasing, escape routing is becoming the main bottleneck in terms of overall routability. For this, we propose novel models and algorithms to solve the escape routing problem in multiple components simultaneously, such that the number of crossings in the intermediate area (between components) is minimized. Finally, we focus on the problem of escape routing within dense pin clusters, which can have arbitrary convex boundaries. We propose a set of sufficient and necessary conditions that guarantee routability outside the escape boundaries. We also discuss how these conditions can be incorporated effectively into an escape routing algorithm.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Hypergraph Models and Algorithms for Data-Pattern Based Clustering

In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depend on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework