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37th IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Saurabh, Kumar Ishii, Masado Khanwale, Makrand A. Sundar, Hari Ganapathysubramanian, Baskar Iowa State Univ Dept Mech Engn Ames IA 50011 USA Univ Utah Sch Comp Salt Lake City UT USA Stanford Univ Ctr Turbulence Res Stanford CA USA

ISBN: (纸本)9798350337662

High-fidelity flow simulations are indispensable when analyzing systems exhibiting multiphase flow phenomena. The accuracy of multiphase flow simulations is strongly contingent upon the finest mesh resolution used to represent the fluid-fluid interfaces. However, the increased resolution comes at a higher computational cost. In this work, we propose algorithmic advances that aim to reduce the computational cost without compromising on the physics by selectively detecting key regions of interest (droplets/filaments) that require significantly higher resolution. The framework uses an adaptive octree-based meshing framework that is integrated with PETSc's linear algebra solvers. We demonstrate scaling of the framework up to 114,688 processes on TACC's Frontera. Finally, we deploy the framework to simulate one of the most resolved simulations of primary jet atomization. This simulation - equivalent to 35 trillion grid points on a uniform grid - is 64x larger than current state-of-the-art simulations and provides unprecedented insights into an important flow physics problem with a diverse array of engineering applications.

关键词： multiphase flow simulations octree massively parallel algorithms adaptive mesh

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parallel Approximation of the Maximum Likelihood Estimation for the Prediction of Large-Scale Geostatistics Simulations

Parallel Approximation of the Maximum Likelihood Estimation ...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Abdulah, Sameh Ltaief, Hatem Sun, Ying Genton, Marc G. Keyes, David E. King Abdullah Univ Sci Technol Extreme Comp Res Ctr Comp Comp Elect & Math Sci & Engn Div Thuwal Saudi Arabia

ISBN: (纸本)9781538683194

Maximum likelihood estimation is an important statistical technique for estimating missing data, for example in climate and environmental applications, which are usually large and feature data points that are irregularly spaced. In particular, the Gaussian log-likelihood function is the de facto model, which operates on the resulting sizable dense covariance matrix. The advent of high performance systems with advanced computing power and memory capacity have enabled full simulations only for rather small dimensional climate problems, solved at the machine precision accuracy. The challenge for high dimensional problems lies in the computation requirements of the hiti-likelihood function, which necessitates O(n(2)) storage and O(n(3)) operations, where n represents the number of given spatial locations. This prohibitive computational cost may be reduced by using approximation techniques that not only enable large-scale simulations otherwise intractable, but also maintain the accuracy and the fidelity of the spatial statistics model. In this paper, we extend the Exascale GeoStatistics software framework (i.e., ExaGeoStat(1)) to support the Tile Low-Rank (TLR) approximation technique, which exploits the data sparsity of the dense covariance matrix by compressing the off-diagonal tiles up to a user-defined accuracy threshold. The underlying linear algebra operations may then be carried out on this data compression formal, which may ultimately reduce the arithmetic complexity of the maximum likelihood estimation and the corresponding memory footprint. Performance results of TLR-based computations on shared and distributed-memory systems attain up to 13X and 5X speedups, respectively, compared to full accuracy simulations using synthetic and real datasets (up to 2M), while ensuring adequate prediction accuracy.

关键词： massively parallel algorithms machine learning algorithms applied computing mathematics and statistics maximum likelihood optimization geo-statistics applications

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GPU-based Image Compression for Efficient Compositing in Distributed Rendering Applications 11

GPU-based Image Compression for Efficient Compositing in Dis...

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IEEE 11th Symposium on Large Data Analysis and Visualization (LDAV)

作者： Lipinksi, Riley Moreland, Kenneth Papka, Michael E. Marrinan, Thomas Univ St Thomas St Paul MN 55105 USA Oak Ridge Natl Lab Oak Ridge TN USA Argonne Natl Lab Argonne IL 60439 USA Northern Illinois Univ De Kalb IL 60115 USA

ISBN: (纸本)9781665432832

Visualizations of large-scale data sets are often created on graphics clusters that distribute the rendering task amongst many processes. When using real-time GPU-based graphics algorithms, the most time-consuming aspect of distributed rendering is typically the compositing phase - combining all partial images from each rendering process into the final visualization. Compositing requires image data to be copied off the GPU and sent over a network to other processes. While compression has been utilized in existing distributed rendering compositors to reduce the data being sent over the network, this compression tends to occur after the raw images are transferred from the GPU to main memory. In this paper, we present work that leverages OpenGL / CUDA interoperability to compress raw images on the GPU prior to transferring the data to main memory. This approach can significantly reduce the device-to-host data transfer time, thus enabling more efficient compositing of images generated by distributed rendering applications.

关键词： Computing methodologies Computer graphics Image compression Computing methodologies parallel computing methodologies parallel algorithms massively parallel algorithms

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GPU algorithms for Fastest Path Problem in Temporal Graphs 24

GPU Algorithms for Fastest Path Problem in Temporal Graphs

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53rd International Conference on parallel Processing (ICPP)

作者： Srikanth, Mithinti Singh, Prashant Ramakrishna, G. Indian Inst Technol Tirupati Tirupati Andhra Pradesh India

ISBN: (纸本)9798400717932

This paper introduces the first GPU-based parallel algorithms to solve the fastest path duration (FPD) problem in temporal graphs. Fastest path duration in temporal graphs is a well studied problem that has multiple use cases in information diffusion, epidemic spreading, and route planning in public transportation. Given a temporal graph G in which each edge associates with a departure time and duration time, and a source vertex s, the Fastest Path Duration (FPD) problem is to compute the journey times from s to all the rest of the vertices in G. The existing multi-core algorithm for FPD by Delling et al. exhibits limited parallelism. In general, many parallel algorithms suffer from doing redundant work while pruning certain computations. Our research focuses on multiple algorithmic ways to avoid redundant work and perform pruning of computations effectively. We introduce three novel GPU-based parallel algorithms for FPD, namely Level Order (LO), Multiple Breadth First Search (MBFS), and Local Work-lists (LW) and implement them on a GPU architecture machine. Our algorithms demonstrate an average speedup of approximately 165 times and a maximum speedup of up to 1383 times over the current state-of-the-art algorithms. This paper provides a comprehensive explanation of various algorithm designs, their optimizations for GPUS, and an extensive evaluation of their performance across various temporal graph scenarios.

关键词： Temporal Graph Fastest Path GPU-algorithms massively parallel algorithms Transformed Graph Journey Time

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faimGraph: High Performance Management of Fully-Dynamic Graphs Under Tight Memory Constraints on the GPU

faimGraph: High Performance Management of Fully-Dynamic Grap...

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International Conference on High Performance Computing, Networking, Storage, and Analysis (SC)

作者： Winter, Martin Mlakar, Daniel Zayer, Rhaleb Seidel, Hans-Peter Steinberger, Markus Graz Univ Technol Graz Austria Max Planck Inst Informat Saarbrucken Germany

ISBN: (纸本)9781538683842

In this paper, we present a fully-dynamic graph data structure for the Graphics Processing Unit (GPU). It delivers high update rates while keeping a low memory footprint using autonomous memory management directly on the GPU. The data structure is fully-dynamic, allowing not only for edge but also vertex updates. Performing the memory management on the GPU allows for fast initialization times and efficient update procedures without additional intervention or reallocation procedures from the host. Our optimized approach performs initialization completely in parallel;up to 300x faster compared to previous work. It achieves up to 200 million edge updates per second for sorted and unsorted update batches;up to 30x faster than previous work. Furthermore, it can perform more than 300 million adjacency queries and millions of vertex updates per second. On account of efficient memory management techniques like a queuing approach, currently unused memory is reused later on by the framework, permitting the storage of tens of millions of vertices and hundreds of millions of edges in GPU memory. We evaluate algorithmic performance using a PageRank and a Static Triangle Counting (STC) implementation, demonstrating the suitability of the framework even for memory access intensive algorithms.

关键词： parallel programming GPU massively parallel algorithms Dynamic graphs

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Comparing Binary-Swap algorithms for Odd Factors of Processes 8

Comparing Binary-Swap Algorithms for Odd Factors of Processe...

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8th IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV)

作者： Moreland, Kenneth Sandia Natl Labs Livermore CA 94550 USA

ISBN: (纸本)9781538668733

A key component of most large-scale rendering systems is a parallel image compositing algorithm, and the most commonly used compositing algorithms are binary swap and its variants. Although shown to be very efficient, one of the classic limitations of binary swap is that it only works on a number of processes that is a perfect power of 2. Multiple variations of binary swap have been independently introduced to overcome this limitation and handle process counts that have factors that are not 2. To date, few of these approaches have been directly compared against each other, making it unclear which approach is best. This paper presents a fresh implementation of each of these methods using a common software framework to make them directly comparable. These methods to run binary swap with odd factors are directly compared. The results show that some simple compositing approaches work as well or better than more complex algorithms that are more difficult to implement.

关键词： Computing methodologies Computer graphics Rendering Computing methodologies parallel computing methodologies parallel algorithms massively parallel algorithms

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A Visual Model for Quality Driven Refinement of Global Illumination 17

A Visual Model for Quality Driven Refinement of Global Illum...

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ACM Symposium on Applied Perception (SAP)

作者： Guenther, Robert Guthe, Stefan Guthe, Michael Bayreuth Univ Univ Str 30 D-95440 Bayreuth Germany Tech Univ Darmstadt Rundeturmstr 12 D-64283 Darmstadt Germany

ISBN: (纸本)9781450351485

When rendering complex scenes using path-tracing methods, long processing times are required to calculate a sufficient number of samples for high quality results. In this paper, we propose a new method for priority sampling in path-tracing that exploits restrictions of the human visual system by recognizing whether an error is perceivable or not. We use the stationary wavelet transformation to efficiently calculate noise-contrasts in the image based on the standard error of the mean. We then use the Contrast Sensitivity Function and Contrast Masking of the Human Visual System to detect if an error is perceivable for any given pixel in the output image. Errors that can not be detected by a human observer are then ignored in further sampling steps, reducing the amount of samples calculated while producing the same perceived quality. This approach leads to a drastic reduction in the total number of samples required and therefore in total rendering time.

关键词： visual model global illumination massively parallel algorithms

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A Deterministic Algorithm for the MST Problem in Constant Rounds of Congested Clique 2021

A Deterministic Algorithm for the MST Problem in Constant Ro...

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53rd Annual ACM SIGACT Symposium on Theory of Computing (STOC)

作者： Nowicki, Krzysztof Univ Copenhagen Copenhagen Denmark Univ Wroclaw Wroclaw Poland

ISBN: (纸本)9781450380539

In this paper we show that the Minimum Spanning Tree problem (MST) can be solved deterministically in O(1) rounds of the Congested Clique model. In the Congested Clique model there are n players that perform computation in synchronous rounds. Each round consist of a phase of local computation and a phase of communication, in which each pair of players is allowed to exchange O(log n) bit messages. The studies of this model began with the MST problem: in the paper by Lotker, Pavlov, Patt-Shamir, and Peleg [SPAA'03, SICOMP'05] that defines the Congested Clique model the authors give a deterministic O(log log n) round algorithm that improved over a trivial O(log n) round adaptation of Boravka's algorithm. There was a sequence of gradual improvements to this result: an O(log log log n) round algorithm by Hegeman, Pandurangan, Pemmaraju, Sardeshmukh, and Scquizzato [PODC'15], an O(log* n) round algorithm by Ghaffari and Parter, [PODC'16] and an O(1) round algorithm by Jurdzinski and Nowicki, [SODA'18], but all those algorithms were randomized. Therefore, the question about the existence of any deterministic o(log log n) round algorithms for the Minimum Spanning Tree problem remains open since the seminal paper by Lotker, Pavlov, Patt-Shamir, and Peleg [SPAA'03, SICOMP'05]. Our result resolves this question and establishes that O(1) rounds is enough to solve the MST problem in the Congested CI iq ue model, even if we are not allowed to use any randomness. Furthermore, the amount of communication needed by the algorithm makes it applicable to a variant of the M PC model using machines with local memory of size O(n).

关键词： Minimum Spanning Tree MST Deterministic algorithms Graph algorithms Distributed algorithms Congested Clique parallel algorithms massively parallel algorithms MapReduce

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FastFlow: GPU Acceleration of Flow and Depression Routing for Landscape Simulation

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COMPUTER GRAPHICS FORUM 2024年第7期43卷

作者： Jain, Aryamaan Kerbl, Bernhard Gain, James Finley, Brandon Cordonnier, Guillaume Univ Cote Azur Inria Nice France TU Wien Vienna Austria Univ Cape Town Cape Town South Africa Univ Lausanne Lausanne Switzerland

Terrain analysis plays an important role in computer graphics, hydrology and geomorphology. In particular, analyzing the path of material flow over a terrain with consideration of local depressions is a precursor to many further tasks in erosion, river formation, and plant ecosystem simulation. For example, fluvial erosion simulation used in terrain modeling computes water discharge to repeatedly locate erosion channels for soil removal and transport. Despite its significance, traditional methods face performance constraints, limiting their broader applicability. In this paper, we propose a novel GPU flow routing algorithm that computes the water discharge in O(log n) iterations for a terrain with n vertices (assuming n processors). We also provide a depression routing algorithm to route the water out of local minima formed by depressions in the terrain, which converges in O(log(2) n) iterations. Our implementation of these algorithms leads to a 5x speedup for flow routing and 34 x to 52 x speedup for depression routing compared to previous work on a 10242 terrain, enabling interactive control of terrain simulation.

关键词： CCS Concepts • Computing methodologies → Shape modeling massively parallel algorithms

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GPU-enabled searches for periodic signals of unknown shape

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ASTRONOMY AND COMPUTING 2022年 38卷 100511-100511页

作者： Gowanlock, M. Butler, N. R. Trilling, D. E. McNeill, A. No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA Arizona State Univ Sch Earth & Space Explorat Tempe AZ 85287 USA No Arizona Univ Dept Astron & Planetary Sci Flagstaff AZ 86011 USA

Recent and future generation observatories will enable the study of variable astronomical phenomena through their time-domain capabilities. High temporal fidelity will allow for unprecedented investigations into the nature of variable objects - those objects that vary in brightness over time. A major bottleneck in data processing pipelines is constructing light curve solutions for catalogs of variable objects, as it is well-known that period finding algorithms are computationally expensive. Furthermore, there are many period finding algorithms that are often suited for specific science cases. In this paper, we present the first GPU-accelerated Super Smoother algorithm. Super Smoother is general purpose and uses cross-validation to fit line segments to a time series, and as such, is more computationally expensive than other algorithms, such as Lomb-Scargle. Because the algorithm requires making several scans over the input time series for a tested frequency, we also propose a novel generalized-validation variant of Super Smoother that only requires a single scan over the data. We compare the performance of our algorithms to analogous parallel multi-core CPU implementations on three catalogs of data, and show that it is generally advantageous to use the GPU algorithm over the CPU counterparts. Furthermore, we demonstrate that our single-pass variant of Super Smoother is roughly equally as accurate at finding correct period solutions as the original algorithm. Our software supports several features, such as batching the computation to eliminate the possibility of exceeding global memory on the GPU, processing a single object or batches of objects, and we allow for scaling the algorithm across multiple GPUs.& nbsp;(c) 2021 Elsevier B.V. All rights reserved.

关键词： Asteroids: general massively parallel algorithms Methods: data analysis Methods: numerical Single instruction multiple data Stars: variables

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