pnnl/ruleset-checking-tool
The Ruleset Checking Tool (RCT) will be a tool for verifying implementation of a ruleset (such as ASHRAE Standard 90.1 Appendix G) in building performance modeling (BPM) tools.
McNeill, James↗
Engineering topics
Central, PNNL Developer
Publications and source records attributed to Central, PNNL Developer.
pnnl/pakman
PaKman: A Scalable Algorithm for Generating Genomic Contigs on Distributed Memory Machines. PaKman presents a fully distributed method that tackles assembly of large genomes through the combinationof a novel data-structure (PaK-Graph) and algorithmic strategies to simplify communication and I/O footprint during the assembly process.
Ghosh, Priyanka↗
pnnl/HiParTI
A Hierarchical Parallel Tensor Infrastructure (HiParTI), is to support fast essential sparse tensor operations and tensor decompositions on multicore CPU and GPU architectures. It consists of sparse tensor decompositions, CANDECOMP/PARAFAC (CP) and Tucker decompositions, fundamental tensor operations, and tensor transformations
Li, Jiajia↗
pnnl/DeepDataProfiler
Toolkit for explaining the decision strategy for neural networks.
Praggastis, Brenda↗
pnnl/QASMBench
A low-level OpenQASM benchmark suite for NISQ evaluation and simulation. Please see our paper for details. n this repository you will find a low-level and light-weighted benchmark suite based on IBM OpenQASM language (see spec). It collects commonly seen quantum algorithms and routines from various domains including chemistry, simulation, linear algebra, searching, optimization, arithmetic, machine learning, fault tolerance, cryptography, etc. QASMBench trades-off between generality and usability, covering the number of qubits ranging from 2 to 60K, and the circuit depth from 4 to 12M
Li, Ang↗
pnnl/safekit
Some of the most pressing machine learning applications such as cyber security and object recognition lack enough ground-truth training data to build a classifier. We solve this problem by using unsupervised deep learning techniques to determine when data were anomalous or deviated from the norm. Traditional methods require either specific context dependent expertise to construct models, or prior examples of events and large amounts of well-labeled data to perform machine learning. Some cutting-edge deep learning approaches have been used to characterize complex events, but these have not been applied to the streaming environment and require a great deal of data. We demonstrated the use of an autoencoder and relevant featurization techniques to both learn a feature space and then identify anomalous portions without the aid of labeled data or domain knowledge. In this way, we demonstrated the detection of anomalous features without domain knowledge or tagged examples, and delivered these features to users.
Central, PNNL Developer↗