#HPCsystems

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IBM Cloud HPC: Cadence-Enabled Next-Gen Electronic Design

Cadence makes use of IBM Cloud HPC

With more than 30 years of experience in computational software, Cadence is a leading global developer in electrical design automation (EDA). Chips and other electrical devices that power today’s growing technology are designed with its assistance by businesses all over the world. The company’s need for computational capacity is at an all-time high due to the increased need for more chips and the integration of AI and machine learning into its EDA processes. Solutions that allow workloads to move between on-premises and cloud environments seamlessly and allow for project-specific customization are essential for EDA companies such as Cadence.

Chip and system design software development requires creative solutions, strong computational resources, and cutting-edge security support. Cadence leverages IBM Cloud HPC with IBM Spectrum LSF as the task scheduler. Cadence claims faster time-to-solution, better performance, lower costs, and easier workload control using IBM Cloud HPC.

Cadence also knows personally that a cloud migration may call for new skills and expertise that not every business has. The goal of the entire Cadence Cloud portfolio is to assist clients worldwide in taking advantage of the cloud’s potential. Cadence Managed Cloud Service is a turnkey solution perfect for startups and small- to medium-sized businesses, while Cloud Passport, a customer-managed cloud option, enables Cadence tools for large enterprise clients.

Cadence’s mission is to make the cloud simple for its clients by putting them in touch with experienced service providers like IBM, whose platforms may be utilized to install Cadence solutions in cloud environments. The Cadence Cloud Passport approach can provide access to cloud-ready software solutions for usage on IBM Cloud, which is beneficial for organizations looking to accelerate innovation at scale.

Businesses have complicated, computational problems that need to be solved quickly in today’s cutthroat business climate. Such issues may be too complex for a single system to manage, or they may take a long time to fix. For businesses that require prompt responses, every minute matters. Problems cannot fester for weeks or months for companies that wish to stay competitive. Companies in semiconductors, life sciences, healthcare, financial services, and more have embraced HPC to solve these problems.

Businesses can benefit from the speed and performance that come with powerful computers cooperating by utilizing HPC. This can be especially useful in light of the ongoing desire to develop AI on an ever-larger scale. Although analyzing vast amounts of data may seem unfeasible, high-performance computing (HPC) makes it possible to employ powerful computer resources that can complete numerous calculations quickly and concurrently, giving organizations access to insights more quickly. HPC is also used to assist companies in launching innovative products. Businesses are using it more frequently because it helps manage risks more effectively and for other purposes.

HPC in the Cloud

Businesses that operate workloads during periods of high activity frequently discover that they are running above the available compute capacity on-premises. This exemplifies how cloud computing may enhance on-premises HPC to revolutionize the way a firm uses cloud resources for HPC. Cloud computing can help with demand peaks that occur throughout product development cycles, which can vary in length. It can also give businesses access to resources and capabilities that they may not require continuously. Increased flexibility, improved scalability, improved agility, increased cost efficiency, and more are available to businesses who use HPC from the cloud.

Using a hybrid cloud to handle HPC

In the past, HPC systems were developed on-site. But the huge models and heavy workloads of today are frequently incompatible with the hardware that most businesses have on-site. Numerous organizations’ have turned to cloud infrastructure providers who have already made significant investments in their hardware due to the large upfront costs associated with acquiring GPUs, CPUs, networking, and creating the data Centre infrastructures required to effectively operate computation at scale.

A lot of businesses are implementing hybrid cloud architectures that concentrate on the intricacies of converting a section of their on-premises data Centre into private cloud infrastructure in order to fully realize the benefits of both public cloud and on-premises infrastructures.

Employing a hybrid cloud strategy for HPC, which combines on-premises and cloud computing, enables enterprises to leverage the advantages of each and achieve the security, agility, and adaptability needed to fulfil their needs. IBM Cloud HPC, for instance, may assist businesses in managing compute-intensive workloads on-site flexibly. IBM Cloud HPC helps enterprises manage third- and fourth-party risks by enabling them to use HPC as a fully managed service, all while including security and controls within the platform.

Looking forward

Businesses can overcome many of their most challenging problems by utilizing hybrid cloud services via platforms such as IBM Cloud HPC. Organization’s adopting HPC should think about how a hybrid cloud strategy might support traditional on-premises HPC infrastructure deployments.

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AtomMap: Unveiling the Secrets of Protein Structures

Google Cloud-scale computation-driven drug discovery: Atommap computational drug discovery company built an elastic supercomputing cluster on Google Cloud for large-scale computation-driven drug discovery.

New medicine introduction usually involves four steps: Target identification locates the protein target linked to the illness; molecular discovery finds the novel molecule that modulates the target’s function; clinical trial evaluates the candidate drug molecule’s safety and efficacy in patients; and commercialization provides the medication to patients.

First, an efficient mechanism to modulate the target function must be established to maximize therapeutic efficacy and minimize side effects. Second, the ideal drug molecule must be designed, chosen, and manufactured to faithfully implement the mechanism, be bioavailable, and have acceptable toxicity.

Why is molecular discovery so difficult?

A protein is always moving due to heat, which modifies its conformation and its ability to connect with other biomolecules, ultimately impacting its functions. Time and again, structural information about a protein’s conformational dynamics points to new avenues for functional regulation. Nevertheless, despite recent enormous advancements in experimental methodologies, such information frequently eludes experimental determination.

The number of different tiny molecules in the chemical “universe” is estimated to be 1060 (Reymond et al., 2010). With perhaps ten billion already manufactured, chemists have roughly 1060 still to go.

Molecular discovery presents two primary obstacles despite its boundless potential: it’s likely that they haven’t explored every mechanism of action or identified the optimal molecules, meaning the could always create a more effective medication.

The computation-driven strategy for molecule discovery used by Atommap

Atommap molecular engineering platform leverages high-performance computing to identify new therapeutic compounds against targets that were previously unreachable through innovative processes. This approach expedites, reduces costs, and increases the likelihood of success in the process.

Atommap platform has significantly cut the time (by more than half) and cost (by 80%) of molecular discovery in previous projects. For instance, it significantly sped up the search for new compounds that degrade highly valuable oncological targets and was crucial in moving a molecule against a difficult therapeutic target to the clinical trial in 17 months (NCT04609579) (Mostofian et al. 2023).

Atommap does this through:

Complicated conformational dynamics of the protein target and its interactions with drug compounds and other biomolecules are revealed by sophisticated molecular dynamics (MD) simulations. They determine the target protein’s dynamics-function link, which is crucial for determining the most effective drug molecule mechanism of action.

Models that generate new molecules by counting them. Google Cloud models start with a three-dimensional blueprint of a drug molecule’s interaction with its target and then computationally generate thousands to hundreds of thousands of new virtual molecules that satisfy both favorable drug-like properties and the desired interactions.

Prediction models that are ML-enhanced and based on physics that can predict molecule potencies and other attributes with accuracy. The computer evaluation of each molecular design takes into account its drug-likeness, target-binding affinity, and effects on the target. As a result, Cloud may investigate many times more compounds than can be synthesized and tested in a lab. They can also carry out several rounds of design while that wait for the results of frequently drawn-out experiments, which shortens turnaround times and raises the likelihood of success.

Molecular Discovery as a Service and Computation as a Service

In order to have a meaningful and widespread influence on drug development, Atommap must combine its extensive knowledge of molecular discovery with outside knowledge of target identification, clinical trials, and commercialization.

They establish partnerships through two methods: Molecular development as a Service (MDaaS, pronounced Midas) and Computation as a Service (CaaS). Google cloud computation-driven molecular engineering platform is easily and affordably accessible to all drug development organizations.

Atommap pay-as-you-go cloud-as-a-service (CaaS) approach allows any discovery project to test their computational tools at a small and reasonable scale without committing a large amount of money, as opposed to selling software subscriptions. While not all projects can be solved computationally, the majority can. With this method, any drug development effort can swiftly and affordably introduce the necessary computations with observable effects, then scale them up to maximise their benefits.

How to construct a Google Cloud elastic supercomputer

Cloud computing platform was moved from their internal cluster to a hybrid environment that incorporates Google Cloud over the course of multiple steps.

Slurm

Slurm was used by numerous workflows in their platform to handle computing activities. They used Google’s open-source Cloud HPC Toolkit to create a cloud-based Slurm cluster in order to move to Google Cloud. A command line utility called Cloud HPC Toolkit makes it simple to set up safe, networked cloud HPC systems. They immediately built up computing workloads for Google cloud discovery projects using their Slurm-native tooling after this Slurm cluster was up and operating in a matter of minutes.

Their DevOps department naturally integrates into best practices with the help of Cloud HPC Toolkit. Google compute clusters are specified as “blueprints” inside of YAML files, which enable us to easily and transparently configure particular features of different Google Cloud products. The Toolkit converts blueprints into input scripts that are run by Terraform, an industry-standard tool from Hashicorp that defines “infrastructure-as-code” that can be version-controlled, committed, and reviewed.

Google Cloud also specified their compute machine image in the blueprint using a startup script that works with Hashicorp’s Packer. This made it simple for us to “bake in” the programmes that are usually required for Google cloud jobs, such conda, Docker, and Docker container images that supply dependencies like PyTorch, AMBER, and OpenMM.

Comparable in accessibility and ease of use to previous Slurm systems they have utilized is the deployed Slurm cloud system. They only pay for what Google Cloud use because the computing nodes are only deployed when requested and spun down when completed; the head and controller nodes, from which they log in and deploy, are the only persistent nodes.

Batch

The cloud-native Google Batch allows us much more freedom in their access to the computational resources when compared to Slurm. Batch can be used to plan cloud resources for lengthy scientific computing workloads because it is a managed cloud job-scheduling service. Batch builds up virtual machines that mount NFS stores or Google Cloud Storage buckets with ease. The latter is especially handy as an output directory for their long-running simulations, as it can house multi-gigabyte MD trajectories.

SURF-Submit, Upload, Run, Fetch

In the majority of computational procedures, a common pattern has surfaced. The sequence and structures of the target protein, a list of small compounds along with their valence and three-dimensional structures, the simulation and model parameters are the first set of sophisticated input files that are included in each task. Second, even on the fastest computers, the majority of computing tasks take hours or even days to complete.

Thirdly, the computer tasks generate output datasets that are subject to several analysis and have a sizable volume.

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