Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models
Authors: Robert McGibbon, Bharath Ramsundar, Mohammad Sultan, Gert Kiss, Vijay Pande
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement our algorithm on GPUs and apply the method to two large protein simulation datasets generated respectively on the NCSA Bluewaters supercomputer and the Folding@Home distributed computing network. |
| Researcher Affiliation | Academia | Robert T. Mc Gibbon RMCGIBBO@STANFORD.EDU Department of Chemistry, Stanford University, Stanford CA 94305, USA Bharath Ramsundar RBHARATH@STANFORD.EDU Department of Computer Science, Stanford University, Stanford CA 94305, USA Mohammad M. Sultan MSULTAN@STANFORD.EDU Department of Chemistry, Stanford University, Stanford CA 94305, USA Gert Kiss GKISS@STANFORD.EDU Department of Chemistry, Stanford University, Stanford CA 94305, USA Vijay S. Pande PANDE@STANFORD.EDU Department of Chemistry, Stanford University, Stanford CA 94305, USA |
| Pseudocode | No | The paper describes the learning algorithm using text and equations but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the public release of its source code. |
| Open Datasets | No | The paper describes using custom-generated datasets from specific supercomputing resources and distributed networks, but does not provide concrete access information (link, DOI, repository, or explicit public availability statement) for these datasets. |
| Dataset Splits | No | The paper describes using a model selection criterion based on convergence of relaxation timescales, but does not provide specific train/test/validation dataset splits. |
| Hardware Specification | Yes | The speedup using our GPU implementation is 15 compared to our optimized CPU implementation and 75 with respect to a standard numpy implementation using K = 16 states on a NVIDIA GTX TITAN GPU / Intel Core i7 4 core Sandy Bridge CPU platform. |
| Software Dependencies | No | The paper mentions software components like 'numpy', 'Open MP', 'SSE2 intrinsics', and 'CUDA kernels' but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | HMMs were constructed with 2 to 6 states. We chose by monitoring the convergence of the relaxation timescales as discussed in Sec. 3.2, and set the L1 fusion penalty heuristically to a default value of λ = 0.01. |