Why Choose This Project?
High-Performance Computing (HPC) clusters are critical for scientific simulations, large-scale computations, data analysis, and AI/ML training. Using Google Cloud HPC, students can deploy a scalable, on-demand computing cluster without investing in physical hardware.
This project helps students learn cloud-based parallel computing, cluster management, and large-scale computation orchestration.
What You Get
-
On-demand deployment of an HPC cluster on Google Cloud
-
Multiple compute nodes for parallel processing
-
Job scheduling and workload management
-
Monitoring of cluster performance and resource usage
-
Scalable infrastructure to add/remove nodes dynamically
-
Centralized storage for input/output data
-
Secure access to HPC resources
Key Features
| Feature |
Description |
| Cluster Deployment |
Deploy multi-node HPC clusters on Google Cloud Compute Engine |
| Parallel Computation |
Run parallel jobs using MPI (Message Passing Interface) or OpenMP |
| Job Scheduling |
Use Slurm or PBS for automated job allocation and management |
| Scalable Nodes |
Add or remove compute nodes based on workload demands |
| Centralized Storage |
Use Google Cloud Storage or Filestore for shared access across nodes |
| Monitoring & Logging |
Track CPU, GPU, memory usage, and job status |
| Secure Access |
SSH and IAM-based authentication for cluster management |
| Cost Optimization |
Use preemptible VMs for cost-effective HPC workloads |
Technology Stack
| Layer |
Tools/Technologies |
| Compute Nodes |
Google Compute Engine VMs, optionally GPU-enabled |
| Job Scheduler |
Slurm / PBS for managing jobs across nodes |
| Parallel Processing |
MPI (OpenMPI) / OpenMP |
| Storage |
Google Cloud Storage / Filestore for shared data |
| Monitoring |
Stackdriver / Google Cloud Monitoring |
| Automation |
Deployment scripts using Terraform / Deployment Manager |
| Authentication |
SSH keys / Google Cloud IAM roles |
| Networking |
VPC, subnets, firewall rules for cluster communication |
Google Cloud Services Used
| Service |
Purpose |
| Compute Engine |
Provision virtual machines for HPC cluster nodes |
| Cloud Storage |
Centralized storage for input/output datasets |
| Filestore |
Shared file system across compute nodes |
| Stackdriver / Monitoring |
Monitor cluster performance and logs |
| Cloud IAM |
Secure access control and permissions |
| Deployment Manager / Terraform |
Automate cluster provisioning |
| GPU-enabled VMs (optional) |
Accelerate computation for AI/ML workloads |
Working Flow
-
Cluster Provisioning
Deploy multiple Compute Engine instances as compute nodes with shared network and storage.
-
Install HPC Software
Configure MPI/OpenMP, job scheduler (Slurm/PBS), and necessary libraries.
-
Upload Input Data
Store input datasets in Cloud Storage or Filestore accessible to all nodes.
-
Submit Jobs
Users submit computational jobs through the job scheduler.
-
Parallel Processing
Compute nodes process tasks in parallel, sharing data as needed.
-
Monitor Performance
Use Stackdriver to monitor CPU, GPU, memory, and network utilization.
-
Collect Results
Output data is aggregated in Cloud Storage or Filestore for analysis.
-
Scale Cluster
Add or remove nodes dynamically based on workload requirements.
Customization
Fully
Security
High
Performance
Fast
Future Updates
Free
Total Buyers
500+
Support
Lifetime
