12 papers
Can Calibration Improve Sample Prioritization?
Ganesh Tata, Gautham Krishna Gudur, Gopinath Chennupati +1
Calibration can reduce overconfident predictions of deep neural networks, but can calibration also accelerate training? In this paper, we show that it can when used to prioritize s…
An Effective Baseline for Robustness to Distributional Shift
Sunil Thulasidasan, Sushil Thapa, Sayera Dhaubhadel +3
Refraining from confidently predicting when faced with categories of inputs different from those seen during training is an important requirement for the safe deployment of deep le…
PPT-Multicore: Performance Prediction of OpenMP applications using Reuse Profiles and Analytical Modeling
Atanu Barai, Yehia Arafa, Abdel-Hameed Badawy +3
We present PPT-Multicore, an analytical model embedded in the Performance Prediction Toolkit (PPT) to predict parallel application performance running on a multicore processor. PPT…
PPT-SASMM: Scalable Analytical Shared Memory Model: Predicting the Performance of Multicore Caches from a Single-Threaded Execution Trace
Atanu Barai, Gopinath Chennupati, Nandakishore Santhi +3
Performance modeling of parallel applications on multicore processors remains a challenge in computational co-design due to multicore processors' complex design. Multicores include…
Machine Learning Enabled Scalable Performance Prediction of Scientific Codes
Gopinath Chennupati, Nandakishore Santhi, Phill Romero +1
We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters…
Decoy Selection for Protein Structure Prediction Via Extreme Gradient Boosting and Ranking
Nasrin Akhter, Gopinath Chennupati, Hristo Djidjev +1
Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of…