MIT SuperCloud
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| MIT SuperCloud | |
|---|---|
| Name | MIT SuperCloud |
| Established | 2010s |
| Location | Cambridge, Massachusetts, Massachusetts Institute of Technology |
| Type | High-performance computing infrastructure |
| Discipline | Computer science, High-performance computing, Data-intensive computing |
MIT SuperCloud is a converged high-performance computing and big-data environment developed at the Massachusetts Institute of Technology to support compute, storage, and analytics workflows across academic, industrial, and governmental collaborations. It integrates technologies from projects and organizations such as Linux, Intel Corporation, NVIDIA, Cray Inc., and research initiatives associated with Lincoln Laboratory and the Computer Science and Artificial Intelligence Laboratory. The system is used by researchers in fields including computational biology, astrophysics, materials science, climate science, and financial engineering.
Overview
The project was conceived to bridge capabilities demonstrated in platforms like Oak Ridge systems, Argonne clusters, and commercial offerings from Amazon Web Services and Google Cloud Platform while maintaining on-premises control for institutions such as the Massachusetts Institute of Technology and partner labs like Lawrence Berkeley National Laboratory and Sandia National Laboratories. It emphasizes integration of compute, parallel file systems akin to Lustre and GPFS, container orchestration influenced by Docker and Kubernetes, and interactive notebook environments comparable to Jupyter. Stakeholders include principal investigators funded by agencies such as the National Science Foundation and the Defense Advanced Research Projects Agency.
Architecture and Components
The architecture combines heterogeneous compute nodes—CPU resources from vendors like Intel Corporation and accelerator nodes from NVIDIA—with high-speed interconnects inspired by InfiniBand deployments found at Argonne National Laboratory. Storage tiers range from parallel file systems analogous to Lustre and GPFS to object stores similar to services by Ceph and designs used by Amazon S3. Resource managers and schedulers draw on concepts from SLURM and Torque, while virtualization and container layers build on KVM and Docker. Networking leverages standards from Internet Engineering Task Force-aligned technologies and hardware from vendors such as Arista Networks and Mellanox Technologies.
Software and Tools
The software stack integrates scientific libraries and frameworks adopted in communities around MPI, OpenMP, CUDA, and OpenCL. Data analytics workflows use tools comparable to Apache Spark, Hadoop, and Dask, with notebook interfaces similar to Jupyter and code management using Git and collaboration via platforms inspired by GitHub. System-level tools include monitoring and telemetry influenced by Prometheus and visualization utilities akin to ParaView and Matplotlib. Security and identity management align with practices from MITRE recommendations and federated authentication models such as those promoted by Internet2 and InCommon.
Use Cases and Applications
Users exploit the environment for workflows found in projects by groups working on Human Genome Project-scale analyses, Large Hadron Collider-style simulation pipelines, and climate modeling similar to efforts at National Center for Atmospheric Research. Applications span computational chemistry comparable to research at Bell Labs, machine learning experiments resembling work at OpenAI and DeepMind, and finance research paralleling efforts at Morgan Stanley and Goldman Sachs. Collaborative initiatives include partnerships with industrial labs like IBM Research and consortiums associated with DOE Office of Science programs.
Performance and Scalability
Performance engineering draws on techniques validated on national systems such as Titan and Summit, including network optimization, storage tuning reflecting lessons from National Energy Research Scientific Computing Center deployments, and job scheduling policies inspired by XSEDE resource management. Benchmarks often reference suites used by organizations like SPEC and community benchmarks similar to High Performance Conjugate Gradients and HPL. Scalability tests have compared throughput and latency characteristics to clouds operated by Amazon Web Services and HPC centers such as Lawrence Livermore National Laboratory.
History and Development
Development was driven by cross-department collaborations at the Massachusetts Institute of Technology involving faculty affiliated with CSAIL and staff from Lincoln Laboratory. Funding and early prototypes were associated with grants from the National Science Foundation and partnerships with technology vendors including Intel Corporation and NVIDIA. The design evolved alongside trends in containerization popularized by Docker and orchestration from Kubernetes and incorporated practices advocated by research initiatives at Berkeley Lab and Argonne National Laboratory.
Governance and Access
Governance models reflect consortia approaches used by projects such as XSEDE and federated access frameworks similar to Internet2 and InCommon, balancing institutional policy from the Massachusetts Institute of Technology with sponsor requirements from agencies like the National Science Foundation and Defense Advanced Research Projects Agency. Access is typically provisioned for principal investigators, collaborators at partner institutions including Harvard University and Boston University, and industrial partners under memoranda of understanding modeled after agreements used by national laboratories. User support and training follow curricula akin to workshops run by ESnet and computational science education efforts at Coursera-partnered universities.