Joint Design of Advanced Computing Solutions for Cancer
NCI-DOE collaboration shaping the future for large-scale computational cancer science
The Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program is a result of three key national initiatives; the Precision Medicine Initiative, the National Strategic Computing Initiative, and the Cancer Moonshot. It was officially announced in June of 2016 as a key federal partnership bringing together the National Cancer Institute (NCI) and the Department of Energy (DOE) in a joint effort to simultaneously accelerate advances in precision oncology and computing. The partnership joins the world-leading expertise of the DOE in high-performance computing and the mission of the NCI to collectively influence the design, capability and workforce enabling future computing solutions to benefit the Nation’s health.
Multiple NCI components, including the Center for Biomedical Informatics and Information Technology (CBIIT), the Division of Cancer Treatment and Diagnosis (DCTD), the Division of Cancer Control and Population Science (DCCPS), and the Frederick National Laboratory for Cancer Research, along with four DOE National Laboratories – Argonne National Laboratory, Oak Ridge National Laboratory, Lawrence Livermore National Laboratory, and Los Alamos National Laboratory are working together to create exascale computing ready tools, algorithms and capabilities to advance frontiers of precision oncology, computational and data science, and advanced computing applied to cancer.
Exemplifying agency cooperation and interdisciplinary collaboration, work is already underway in three pilot efforts to accelerate methods to identify promising new treatments; deepen understanding of cancer biology; and understand the impact of new diagnostics, treatments and patient factors in cancer outcomes. Recently announced, the CANcer Distributed Learning Environment (CANDLE) DOE exascale computing project, will deliver essential new computing capabilities that will support the pilots while providing insight into scalable machine learning tools; deep learning, simulation and analytics to reduce time to solution; and inform design of future computing solutions.