105 results on '"Nathan R. Tallent"'
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2. MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs.
3. Graph Analytics on Jellyfish topology.
4. Automatic Extraction of Network Configurations for Realistic Simulation and Validation.
5. Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows.
6. Overcoming Memory Constraints in Quantum Circuit Simulation with a High-Fidelity Compression Framework.
7. Final Report for CHESS: Cloud, High-Performance Computing, and Edge for Science and Security.
8. OPDR: Order-Preserving Dimension Reduction for Semantic Embedding of Multimodal Scientific Data.
9. SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation.
10. Data Flow Lifecycles for Optimizing Workflow Coordination.
11. Accelerating matrix-centric graph processing on GPUs through bit-level optimizations.
12. MemGaze: Rapid and Effective Load-Level Memory Trace Analysis.
13. QuaL2 M: Learning Quantitative Performance of Latency-Sensitive Code.
14. Bit-GraphBLAS: Bit-Level Optimizations of Matrix-Centric Graph Processing on GPU.
15. Characterizing Performance of Graph Neighborhood Communication Patterns.
16. WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling.
17. Diolkos: improving ethernet throughput through dynamic port selection.
18. Effectively Using Remote I/O For Work Composition in Distributed Workflows.
19. Vertex Reordering for Real-World Graphs and Applications: An Empirical Evaluation.
20. Rapid Memory Footprint Access Diagnostics.
21. EXAGRAPH: Graph and combinatorial methods for enabling exascale applications.
22. Rapidly Measuring Loop Footprints.
23. TAZeR: Hiding the Cost of Remote I/O in Distributed Scientific Workflows.
24. Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect.
25. Scaling Deep Learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing.
26. ReWorDs 2022 Keynote: Towards Orchestrating Distributed & Data-Intensive Workflows.
27. Optimizing Distributed Data-Intensive Workflows.
28. Stochastic Programming Approach for Resource Selection Under Demand Uncertainty.
29. Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows.
30. Tartan: Evaluating Modern GPU Interconnect via a Multi-GPU Benchmark Suite.
31. Scaling Deep Learning Workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing.
32. Generating Performance Models for Irregular Applications.
33. Evaluating On-Node GPU Interconnects for Deep Learning Workloads.
34. Geomancy: Automated Performance Enhancement through Data Layout Optimization.
35. Single-node partitioned-memory for huge graph analytics: cost and performance trade-offs.
36. Algorithm and Architecture Independent Benchmarking with SEAK.
37. Fault Modeling of Extreme Scale Applications Using Machine Learning.
38. Modeling the Impact of Silicon Photonics on Graph Analytics.
39. Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect.
40. Diagnosing the causes and severity of one-sided message contention.
41. Power and performance trade-offs for Space Time Adaptive Processing.
42. Towards efficient scheduling of data intensive high energy physics workflows.
43. A case for application-oblivious energy-efficient MPI runtime.
44. Palm: easing the burden of analytical performance modeling.
45. Using Sampling to Understand Parallel Program Performance.
46. Scalable fine-grained call path tracing.
47. Analyzing lock contention in multithreaded applications.
48. Scalable Identification of Load Imbalance in Parallel Executions Using Call Path Profiles.
49. Effectively Presenting Call Path Profiles of Application Performance.
50. Effective performance measurement and analysis of multithreaded applications.
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