Search Results - "Tallent, Nathan R."

Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect by Li, Ang, Song, Shuaiwen Leon, Chen, Jieyang, Li, Jiajia, Liu, Xu, Tallent, Nathan R., Barker, Kevin J.

“…High performance multi-GPU computing becomes an inevitable trend due to the ever-increasing demand on computation capability in emerging domains such as deep…”
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Characterizing Performance of Graph Neighborhood Communication Patterns by Ghosh, Sayan, Tallent, Nathan R., Halappanavar, Mahantesh

“…Distributed-memory graph algorithms are fundamental enablers in scientific computing and analytics workflows. A majority of graph algorithms rely on the graph…”
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MemGaze: Rapid and Effective Load-Level Memory Trace Analysis by Kilic, Ozgur O., Tallent, Nathan R., Suriyakumar, Yasodha, Xie, Chenhao, Marquez, Andres, Eranian, Stephane

“…A challenge of memory trace analysis is combining detailed analysis and low overhead measurement. Currently, hardware/software-based analysis of load-level…”
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EXAGRAPH: Graph and combinatorial methods for enabling exascale applications by Acer, Seher, Azad, Ariful, Boman, Erik G, Buluç, Aydın, Devine, Karen D., Ferdous, SM, Gawande, Nitin, Ghosh, Sayan, Halappanavar, Mahantesh, Kalyanaraman, Ananth, Khan, Arif, Minutoli, Marco, Pothen, Alex, Rajamanickam, Sivasankaran, Selvitopi, Oguz, Tallent, Nathan R, Tumeo, Antonino

“…Combinatorial algorithms in general and graph algorithms in particular play a critical enabling role in numerous scientific applications. However, the…”
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Rapidly Measuring Loop Footprints by Kilic, Ozgur O., Tallent, Nathan R., Friese, Ryan D.

“…Knowing a loop's footprint - the unique data items it accesses - enables important locality and capacity analysis. Unfortunately, current methods for computing…”
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Identifying Performance Bottlenecks in Work-Stealing Computations by Tallent, N.R., Mellor-Crummey, J.M.

“…Work stealing is an effective load-balancing strategy for multithreading, but when computations based on it underperform, traditional tools can't explain why…”
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ReWorDs 2022 Keynote: Towards Orchestrating Distributed & Data-Intensive Workflows by Tallent, Nathan R.

“…Scientific exploration and hypothesis generation is increasingly dependent on the convergence of scientific modeling, data analytics, and machine learning. The…”
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Vertex Reordering for Real-World Graphs and Applications: An Empirical Evaluation by Barik, Reet, Minutoli, Marco, Halappanavar, Mahantesh, Tallent, Nathan R., Kalyanaraman, Ananth

“…Vertex reordering is a way to improve locality in graph computations. Given an input (or "natural") order, reordering aims to compute an alternate permutation…”
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Graph Analytics on Jellyfish topology by Newaz, Md Nahid, Ghosh, Sayan, Suetterlein, Joshua, Tallent, Nathan R., Atiqul Mollah, Md, Ming, Hua

“…Because large unstructured datasets are important for many science domains, distributed graph analytics is critical to many scientists. Unfortunately,…”
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Effectively Presenting Call Path Profiles of Application Performance by Adhianto, Laksono, Mellor-Crummey, J, Tallent, Nathan R

“…Call path profiling is a scalable measurement technique that has been shown to provide insight into the performance characteristics of complex modular…”
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QuaL2 M: Learning Quantitative Performance of Latency-Sensitive Code by Sathanur, Arun, Tallent, Nathan R., Konsor, Patrick, Koyanagi, Ken, McLaughlin, Ryan, Olivas, Joseph, Chynoweth, Michael

“…Quantitative performance predictions are more informative than qualitative. However, modeling of latency-sensitive code, with cost distributions of high…”
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Rapid Memory Footprint Access Diagnostics by Kilic, Ozgur O., Tallent, Nathan R., Friese, Ryan D.

“…Footprint and reuse distance measure temporal locality and therefore do not capture the significance of access patterns (spacial locality). A strided access…”
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Effectively Using Remote I/O For Work Composition in Distributed Workflows by Friese, Ryan D., Mutlu, Burcu O., Tallent, Nathan R., Suetterlein, Joshua, Strube, Jan

“…Distributed scientific workflows are becoming more important with the interest in incorporating AI into their loops. A critical programming and performance…”
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TAZeR: Hiding the Cost of Remote I/O in Distributed Scientific Workflows by Suetterlein, Joshua, Friese, Ryan D., Tallent, Nathan R., Schram, Malachi

“…Many scientific workflows access data derived from specialized instruments. When the data is analyzed, it is accessed over wide area networks, creating…”
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Geomancy: Automated Performance Enhancement through Data Layout Optimization by Bel, Oceane, Chang, Kenneth, Tallent, Nathan R., Duellmann, Dirk, Miller, Ethan L., Nawab, Faisal, Long, Darrell D. E.

“…The size and complexity of large storage systems, such as high-performance computing (HPC) systems, inhibit rapid effective restructuring of data layouts to…”
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SAM-I-Am: Semantic boosting for zero-shot atomic-scale electron micrograph segmentation by Abebe, Waqwoya, Strube, Jan, Guo, Luanzheng, Tallent, Nathan R., Bel, Oceane, Spurgeon, Steven, Doty, Christina, Jannesari, Ali

“…Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics — where…”
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Fault Modeling of Extreme Scale Applications Using Machine Learning by Vishnu, Abhinav, van Dam, Hubertus, Tallent, Nathan R., Kerbyson, Darren J., Hoisie, Adolfy

“…Faults are commonplace in large scale systems. These systems experience a variety of faults such as transient, permanent and intermittent. Multi-bit faults are…”
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Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing by Gawande, Nitin A., Daily, Jeff A., Siegel, Charles, Tallent, Nathan R., Vishnu, Abhinav

“…Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors – including NVIDIA, Intel, AMD, and IBM – have…”
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MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs by Sarkar, Aishwarya, Ghosh, Sayan, Tallent, Nathan R., Jannesari, Ali

“…Graph Neural Networks (GNN) are indispensable in learning from graph-structured data, yet their rising computational costs, especially on massively connected…”
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Accelerating matrix-centric graph processing on GPUs through bit-level optimizations by Chen, Jou-An, Sung, Hsin-Hsuan, Shen, Xipeng, Tallent, Nathan R., Barker, Kevin J., Li, Ang

“…Even though it is well known that binary values are common in graph applications (e.g., adjacency matrix), how to leverage the phenomenon for efficiency has…”
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