In this webinar, we will demonstrate Fuzzball (HPC 2.0) workflows including:
Dask is a Python package allowing for the easy parallelization of certain code and data structures; it also integrates with components of other well-known Python packages like pandas and NumPy. In this demo, we’ll be looking at a simple script that does some numerical calculations (perhaps for some kind of quantitative finance task) that are parallelized across a single node with Dask, which we’ll then use a Fuzzball workflow to run multiple instances of over a set of input data in an embarrassingly parallel way.
QMCPACK and Quantum ESPRESSO
QMCPACK is an open-source electronic structure code written in C++ that supports quantum mechanics research by providing implementations of numerous Quantum Monte Carlo (QMC) algorithms. Quantum ESPRESSO is an open source ab initio quantum chemistry software written in Fortran 90 and C that enables nanoscale-level materials modeling and electronic structure calculations. In this demo, we’ll watch Fuzzball workflows run sample simulations for both QMCPACK and Quantum ESPRESSO, in each case over four NVIDIA V100 GPU-enabled cloud compute nodes at once.
Paraphrased from https://qmcpack.readthedocs.io/en/dev…, https://www.quantum-espresso.org/, and https://en.wikipedia.org/wiki/Quantum…
About Fuzzball: HPC-2.0
“HPC2.0 – The Next Generation of High Performance Computing”
Imagine a computing environment so powerful that it can orchestrate workflows, services, and data while maintaining supply chain integrity from on premise, to cloud, and to the edge. Including the ability to support multiple systems and multiple clouds, federated into a virtual cloud, where every workload lands based on architecture availability, cost, and data management policies.
This is Fuzzball
Integrate multiple HPC resources into One:
Geographically dispersed on-premise supercomputers, Fuzzball is designed as a cloud native and hybrid, federated computing platform for unification of geographically distributed HPC instances whether on-premise or cloud.
Meta scheduling & orchestration across hybrid resources:
Orchestration across architectures and resources.
Scheduling based on cost of compute, data & availability of compute.
Data (including: Locality, Mobility, Gravity, and Security).
Cloud-based HPC clusters and nodes:
HPC resources can be on-premise, multi-premise, cloud, multi-cloud, and federated.
Cloud based resources are elastic based on jobs, resource policies & data.
Our platform supports all major clouds natively, Kubernetes, as well as custom cloud resources.
A unified workload and resource management platform:
Unifying the end-user and administrator experience no matter where you utilize the platform is a design principle. A user interface (UI) application for the submission, tracking, and management of HPC jobs and workflows which is completely API driven with a command line interface as well as a GUI interface. This provides monitoring and management of all HPC resources leveraging standard enterprise monitoring and management of all resources in our platform.
“Fuzzball is readily capable of expanding to integrate new physical or cloud-based HPC assets down to the node level, even the component level. Fuzzball can be easily enhanced with FPGAs and GPUs or the newest network protocols to take advantage of enhancements as they come to market. No more being locked in by anyone or anything.”
To learn more, email us at [email protected]