CSC's computing environment consists of supercomputers Puhti and Mahti, and the quantum learning machine Kvasi. Puhti and Mahti have a fairly similar compute environment, and there is a wide range of workloads that can utilize both efficiently. At the same time their hardware is different, and this makes some worklods uniquely suitable for either Puhti or Mahti.
The Puhti supercomputer, Atos BullSequana X400 cluster based on Intel CPUs, was launched on September 2, 2019. It has a powerful CPU partition with almost 700 nodes with a range of memory sizes and local storage options, all connected with a fast interconnect. Puhti allows the user to reserve compute and memory resources flexibly, and the user can run anything from interactive single core data processing to medium scale simulations spanning multiple nodes.
There are also 80 GPU nodes, with total of 320 Nvidia Volta V100 GPUs. This partition is suitable for all kinds workloads capable of utilizing GPUs, even heavy AI models that span multiple nodes.
Puhti has wide selection of scientific software installed.
The Mahti supercomputer, Atos BullSequana XH2000 system based on AMD CPUs, was launched on August 26, 2020. Mahti is designed for massively parallel jobs requiring high floating point performance and a fast interconnect. The system has in total 1404 nodes equipped with powerful AMD Rome CPUs. These are connected with a fast interconnect, allowing jobs to scale across the full system. In Mahti user reserves full nodes so that the jobs can extract full performance from each node. Mahti is in particular geared towards medium to large scale simulations requiring Petaflops of compute power. Also smaller parellel workloads that are able to use full nodes efficiently can utilize Mahti.
There are also 24 GPU nodes, with total of 96 Nvidia Ampere A100 GPUs. This partition is suitable for all kinds workloads capable of utilizing GPUs, even heavy AI models that span multiple nodes.
The selection of installed scientific software in Mahti is more limited than in Puhti.
The Quantum Learning Machine
Quantum computers differ from their classical counterparts when it comes to the basic computational operators. Before QPUs can be utilized, they require tailor-made programs and algorithms. With Kvasi, the user can explore and develop algorithms for quantum computers. Read here detailed instructions on how to access
Kvasi provides an ecosystem for developing and simulating quantum algorithms in both ideal, and realistic, noisy conditions. With Kvasi, you can optimize your algorithm for a specific hardware (QPU), with specific connections and basic gate operations.
The algorithms can be developed either at a level close to the hardware, using the Atos Quantum Assembler (AQASM) language, or using a higher level, Python based language and ready-made libraries. The QLM comes with several ready-made examples. You can also download and run locally myQLM - a light-weight version of the QLM ecosystem.
Last edited Tue Apr 27 2021