High-Performance Computing (HPC)
High-Performance Computing for HPC is an aggregation of computing power to solve those problems. Which are either too large for a standard computer. Or it will take too long. For this reason, It also called supercomputing and on the other hand, It enables the simulation. or analysis of large amounts of data. Which would not be possible with other standard computers.
Why use HPC/HPC Challenges?
This is the common challenge with high-performance computing. That you are producing or processing more data than your infrastructure resources. You have to wait weeks or months to see results. Which usually delays innovation and slows down research.
How does High-Performance Computing (HPC) work?
You can think of a high performance computing system called a cluster as a cluster of computers. And each computer in the cluster is called a node.
Each unit in the cluster has more than one core. One is an operating system for processors, storage, and units to talk to each other with networking capabilities.
A small cluster, for example, may have 16 cores with 64 cores. Which are four cores per processor. So that you can solve the problem very fast.
A major change is a supercomputer. An HPC job that will run on an on-premises cluster for three months. Cloud can run from 16,000 cores to 16,000 hours with little or no incremental cost.
And with Google Cloud enhancing your HPC environment. You achieve economies of scale with access to the largest compute and storage hardware, global presence, robust networking. It also has intelligent automated management capabilities on the cleanest cloud.
How to build an HPC environment on google cloud?
Compute, storage, and networking are the building blocks of high-performance computing.
Compute Engine – High-Performance Computing (HPC)
It Compute Engine provides customizable virtual machines running in Google Cloud.
Machine Type Family
You can scale up and down as needed and choose several types of machines for your workload. We recommend compute-optimized C2 machines for most high-performance computing applications. But if you want to use GPU. So you can also select general-purpose N1, N2, or N2D. For applications requiring larger memory sizes or A2 instances.
Custom Machine Type
For very specific requirements. You can choose custom machine types with the exact number of cores and memory to match your workload’s requirements and get the best performance per dollar.
Preemptable VMs – High-Performance Computing (HPC)
You could also use cost-effective preemptable VMs. Which are short-lived compute instances suitable for batch jobs and fault-tolerant workloads.
Storage options in Google Cloud
Now, the underlying storage system is critical to the performance of many HPC applications. You have a lot of storage options in Google Cloud. Cloud storage is a highly scalable object store to store data of any amount. Continuous disk is durable and high-performance block storage for your VM instances. Philistore High Scale is a high-performance scale-out system. This makes it easier to mount file shares on the compute engine VM.
Networking ensures Google’s privately managed global network infrastructure. Your data and applications be at least exposed to the public Internet.
Virtual private cloud (VPC) networks are available to enable connectivity to your Compute Engine VM instance and to configure firewalls for your application.
Placement Policy – High-Performance Computing (HPC)
With placement policies, you can control the placement of your VMs in our data centers. The compact placement policy provides low latency between nodes by simultaneously placing instances. Within the same network infrastructure to speed communication between nodes.
HPC workload in Google cloud
Now, put it together for your HPC workload on Google Cloud. First, determine the compute storage and networking requirements for your HPC code. It creates your HPC cluster using compute engine instances. It associated with the storage of your choice.
Additionally, Google Cloud supports many job schedulers. Which makes it easy for you to start an autoscale virtual machine to meet the requirements of the job or to spin the cluster upon completion of the job. Which helps in saving costs. Let’s imagine your result in a BigQuery or AI platform for post-processing. From there, just monitor performance and change the cluster as needed.
Security for HPC workload
Security is important for any HPC workload. Google Cloud’s secure by design infrastructure protects your data, applications, and users with advanced antimalware and threat detection.
HPC use cases
The latest performances range from presenting the visual effects of a blockbuster film to curing diseases. And in financial services, there is much more to risk analysis. Or even to design the next generation of cars. High-performance computing drives research, development, and innovation in many industries.
If this has piqued your interest, visit Google cloud to learn more and get started.