iNFORMER Smart is seen to outperform Spark in other benchmark algorithms as shown below:
Benchmark Setup:
Number of Iterations: 1000
Input Data Size: 2 GB
One MPI Process Per Node.
8 Threads per Node.
Back to iNFORMER Smart.
RNET has recently been awarded a Department of Energy Phase II SBIR to develop a Machine Learning Based Data Compression (MLDC) algorithm for numeric simulation data.
RNET has recently been awarded a NASA Phase I SBIR to develop a Rapid Data Analytics Platform using machine learning uses a novel dimensional reduction algorithm.
RNET has recently been awarded an NIH Phase II SBIR to design and develop Machine Learning tools to help Pathologists overcome the limitations of current computing hardware to design more accurate deep learning models for use in clinical diagnostics. These models will be able to analyze very large digitized images of glass slides (i.e., Whole Slide Images) to aid pathologists in tasks like cancer detection. The ability to analyze these images in their entirety instead of in small parts will improve the diagnostic accuracy of models and will accelerate algorithm development efforts.
iNFORMER Smart is seen to outperform Spark in other benchmark algorithms as shown below:
Number of Iterations: 1000
Input Data Size: 2 GB
One MPI Process Per Node.
8 Threads per Node.
Back to iNFORMER Smart.