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  • GPU accelerated voxel-based machining simulation

    May 08, 2021 · The simulation of subtractive manufacturing processes has a long history in engineering. Corresponding predictions are utilized for planning, validation and optimization, e.g., of CNC-machining processes. With the up-rise of flexible robotic machining and the advancements of computational and algorithmic capability, the simulation of the coupled machine-process behaviour for complex …

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  • Open Collections - UBC Library Open Collections

    UBC Library's Open Collections include digital photos, books, newspapers, maps, videos, theses and more. These publicly-accessible collections are constantly growing and reflect the research interests of the UBC community and beyond. Learn More About UBC's Open Collections. Elevate your research game with the Open Collections Research API.

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  • How to Get Started with OptiX 7 | NVIDIA Developer Blog

    Any-hit programs: Invoked during traversal when a potential primitive intersection is encountered. This allows the application to reject intersections, terminate traversal, or gather information about primitives encountered during traversal. Care should be taken when using any-hit programs as they come with a high performance cost on RTX hardware.

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  • (PDF) Towards Efficient Graph Traversal using a Multi- GPU

    Processors Dual Intel Xeon E5-2650 with 64 GB Above nodes have: D. Distributed Multi-GPU Approach The distributed graph traversal has been implemented on memory Operating system CentOS 6.7. multi-GPU cluster, following and eventually extending the work presented by …

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  • 2019 IEEE International Parallel and Distributed

    GPUs for Graph and Predictive Analytics | NVIDIA Developer

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  • (PDF) Towards Efficient Graph Traversal using a Multi- GPU

    Processors Dual Intel Xeon E5-2650 with 64 GB Above nodes have: D. Distributed Multi-GPU Approach The distributed graph traversal has been implemented on memory Operating system CentOS 6.7. multi-GPU cluster, following and eventually extending the work presented by …

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  • ReGra: Accelerating Graph Traversal Applications Using

    H. Liu et al.: ReGra: Accelerating Graph Traversal Applications Using ReRAM With Lower Communication Cost process graphs and solve the issue of limited bandwidth and data movement. Another problem that comes with PIM architecture is communication among processing cubes. In PIM architec-ture, multiple cubes are usually used for graph storage and

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  • SAIL Research @ SJTU

    Mar 22, 2016 · This lead to the development of MapGraph, a high-level API for GPU-accelerated graph analytics, in 2014. We first started using libraries like moderngpu, cub, and others in our software, which we still use today. Building on prior success in scalable graph traversal on GPUs, which showed the potential for graphs on GPUs and with DARPA funding

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  • PSL: Exploiting Parallelism, Sparsity and Locality to

    Jun 09, 2020 · Abstract. Matrix factorization is a basis for many recommendation systems. Although alternating least squares with weighted-(lambda )-regularization (ALS-WR) is widely used in matrix factorization with collaborative filtering, this approach unfortunately incurs insufficient parallel execution and ineffective memory access.Thus, we propose a solution for accelerating the ALS-WR algorithm by

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  • [PDF] A Distributed Multi-GPU System for Fast Graph

    We present Lux, a distributed multi-GPU system that achieves fast graph processing by exploiting the aggregate memory bandwidth of multiple GPUs and taking advantage of locality in the memory hierarchy of multi-GPU clusters. Lux provides two execution models that optimize algorithmic efficiency and enable important GPU optimizations, respectively.

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  • dblp: Chao Li 0009

    Nov 06, 2021 · Unleashing the Scalability Potential of Power-Constrained Data Center in the Microservice Era. ICPP 2019: 10:1-10:10 Excavating the Potential of GPU for Accelerating Graph Traversal. IPDPS 2019: 221-230 [c30] view. Bridging the Semantic Gaps of GPU Acceleration for Scale-out CNN-based Big Data Processing: Think Big, See Small. PACT 2016

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  • A Method of Accelerating LDA Program with GPU

    Extensive experiments show that with 2x-18x compression rate, our proposed GPU-based graph traversal on compressed graphs (GCGT) achieves competitive efficiency compared with the state-of-the-art

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  • GPU-Accelerated Graph Label Propagation for Real-Time

    GPU-accelerated Graph Processing. Recently, there are rapid growing interests in employing GPUs to accelerate a variety of graph processing workloads, e.g., graph traversal [21, 23], pager-ank [13, 30] and network motif detection [12, 20]. These existing works leverage the associative property of …

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  • GPU-Accelerated High-Level Synthesis for Bitwidth

    Future Work • Do more work per GPU thread, only save best, local merge operations — better use of GPU threads • Affine analysis formulations for GPU parallelism — Affine analysis formulations for …

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  • Grus: Toward Unified-memory-efficient High-performance

    Session 6: GPU Computing I Excavating the Potential of GPU for Accelerating Graph Traversal 221 Pengyu Wang (Shanghai Jiao Tong University), Lu Zhang (Shanghai Jiao Tong University), Chao Li (Shanghai Jiao Tong University), and Minyi Guo (Shanghai Jiao Tong University) vii.

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  • GraphLily: Accelerating Graph Linear Algebra on HBM

    Apr 16, 2019 · Jan 24, 2019 : Our paper Excavating the Potential of GPU for Accelerating Graph Traversal is accepted by IPDPS 2019 which will be held in Rio de Janeiro, Brazil Jan 14, 2019: The personal webpage is released!

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  • GPU-based Graph Traversal on Compressed Graphs

    Jun 25, 2019 · In this paper, we introduce GPU-based graph traversal on compressed graphs, so as to enable the processing of graphs having a larger size than the device memory. Designed towards GPU's SIMT architecture, we propose two novel parallel scheduling strategies Two-Phase Traversal and Task-Stealing to handle thread divergence and workload imbalance

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  • Grus: Toward Unified-memory-efficient High-performance

    Excavating the potential of GPU for accelerating graph traversal. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium (IPDPS'19) . 221–230. Yangzihao Wang, Andrew A. Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, and John D. Owens. 2015.

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  • Accelerating GPU Betweenness Centrality | August 2018

    Accelerating GPU Betweenness Centrality. Graphs that model social networks, numerical simulations, and the structure of the Internet are enormous and cannot be manually inspected. A popular metric used to analyze these networks is Betweenness Centrality (BC), which has applications in community detection, power grid contingency analysis, and

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  • BFS-4K: An Efficient Implementation of BFS for Kepler GPU

    Jun 12, 2014 · Breadth-first search (BFS) is one of the most common graph traversal algorithms and the building block for a wide range of graph applications. With the advent of graphics processing units (GPUs), several works have been proposed to accelerate graph algorithms and, in particular, BFS on such many-core architectures. Nevertheless, BFS has proven to be an algorithm for which it is hard to obtain

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