From Genome Data to Drug Discovery: How a Biotech Firm Scales HPC with Flatcar in the Cloud
Hybrid HPC for Biotech Breakthroughs: On-Prem GPUs + AWS Flatcar Nodes
Introduction
In the world of biotechnology, high-performance computing (HPC) plays a crucial role in accelerating tasks ranging from genomic sequencing to drug discovery simulations. For labs and research organizations, the ability to rapidly crunch massive datasets can be the difference between groundbreaking insight and months of delays.
Yet HPC clusters with powerful GPU resources can be both expensive to acquire and challenging to maintain. Additionally, peak usage often exceeds on-premise capacity, while quieter times leave expensive hardware partially idle. Seeking an agile solution, a biotech firm (“BioCompute Labs”) architected a hybrid HPC environment: On-prem GPU clusters handle core workloads, while less-critical or bursty tasks automatically spin up on AWS-based Flatcar Container Linux nodes. This approach harnesses the reliability of local HPC systems for mission-critical computations, while leveraging cloud elasticity for surge capacity—yielding a balanced, cost-effective HPC pipeline.
Background: BioCompute Labs and Their Evolving HPC Needs
From Single-Rack Servers to Massive Data Sets
BioCompute Labs began as a small research startup, analyzing relatively modest datasets derived from clinical trials and smaller gene-expression studies. Over several years, the firm grew, forging partnerships with larger pharmaceutical and academic institutions. Suddenly, they faced:
Exponential Data Growth: Sequencing entire genomes can generate terabytes of raw data.
Complex Simulations: AI-driven drug discovery tasks require GPU-intensive computing that can saturate an on-prem cluster.
Peak Periods: Funding from new research grants or big pharma collaborations can trigger temporary usage spikes for HPC modeling.
Existing On-Prem GPU Cluster
To handle core HPC tasks, BioCompute Labs invested in an on-premise GPU cluster:
Nodes: 16 servers, each equipped with 4 high-end GPUs (e.g., NVIDIA A100 or V100).
Storage: A high-throughput parallel file system for local HPC workflows.
Scheduler: Slurm for job management, though some new microservices also used Kubernetes or Docker-based pipelines.
In typical HPC fashion, the cluster performed well for “base load” work. However, new projects frequently led to queue backlogs, forcing priority decisions between critical workloads.
Motivation for Hybrid HPC
Cost vs. Capacity: Building a second on-prem HPC cluster to handle peak usage would be capital-intensive, and idle hardware outside peak periods would be underutilized.
Elastic Cloud Resources: On-demand instances in AWS can spin up GPU or CPU nodes as needed, paying only for usage.
Containerization Trend: Some HPC modeling components moved to container-based solutions, making them easier to offload to cloud-based container nodes.
Flatcar Container Linux emerged as a key element in their AWS environment, selected for its lightweight footprint, automatic updates, and strong container orchestration synergy.
Architecture Overview
On-Prem GPU Cluster
Hardware: 16 GPU-equipped nodes, InfiniBand fabric for fast inter-node communication, Slurm scheduler.
OS: A specialized HPC distro (e.g., CentOS or Rocky Linux) tuned for GPU drivers and HPC libraries.
AWS Extension
Flatcar Container Linux Nodes: Dynamically provisioned in AWS using Terraform, each node tuned for Docker or containerd-based HPC microservices.
Auto Scaling Groups (ASG): Spin up more or fewer Flatcar nodes depending on HPC job queue length.
VPN or Direct Connect: Secure link between the on-prem cluster and AWS VPC. This allows the HPC scheduler (or custom job pipeline) to orchestrate tasks across both local and cloud resources.
Step-by-Step Process
1. Containerizing HPC Workloads
Many HPC applications were previously compiled in static binaries or installed with environment modules. Under the new approach:
Dockerfiles or container images for HPC tasks (e.g., molecular dynamics, gene alignment, AI/ML frameworks) ensure consistent runtimes.
Some GPU workloads require NVIDIA Container Toolkit on both the on-prem cluster and AWS.
HPC jobs are scheduled via Slurm, which either runs them locally or triggers a workflow that spins up AWS nodes if queue length or job wait times exceed certain thresholds.
Command Output example (simplified Docker build for a GPU-based HPC container):
FROM nvidia/cuda:11.3-devel-ubuntu20.04
RUN apt-get update && apt-get install -y python3 python3-pip
# Install HPC library
RUN pip3 install torch==2.0.0 # as an example
COPY my_ml_code.py /workspace/
ENTRYPOINT [ "python3", "my_ml_code.py" ]
2. Provisioning Flatcar Nodes on AWS
Terraform scripts define how many Flatcar Container Linux nodes to launch, each time specifying:
AMI: A recent stable or LTS Flatcar image for the region.
Instance Type: Varies—some tasks need GPU instances (p2, p3, or p4 in AWS). Others just need CPU-based c5 or m5 instances for less-critical HPC tasks.
Ignition: Configures Docker or containerd, sets up credentials, registers the node with the HPC job pipeline.
Terraform Snippet:
data "aws_ami" "flatcar" {
most_recent = true
owners = ["075585003325"] # example Flatcar account ID
filter {
name = "name"
values = ["Flatcar-stable-*"]
}
}
resource "aws_instance" "hpc_nodes" {
count = var.node_count
ami = data.aws_ami.flatcar.id
instance_type = var.instance_type
user_data = file("${path.module}/flatcar_ignition.json")
# ...
}
3. Connectivity and Job Scheduling
VPN or Direct Connect ensures a secure tunnel between on-prem HPC and AWS. A small custom script or an extended Slurm plugin:
Monitors the local HPC job queue length.
If the queue surpasses a threshold or certain job types are flagged “offload-friendly,” it triggers an AWS scaling event.
Once AWS nodes are online, the HPC job or containerized workflow is distributed across the newly added resources.
Data either streams from on-prem storage or is staged on S3/EFS for ephemeral usage.
Trade-Off: Minimizing data movement is crucial—some tasks are too large to feasibly transfer. Those remain on-prem, while smaller or less data-intensive tasks run in the cloud.
4. Observability and Metrics
Prometheus: Deployed on both on-prem cluster and cloud-based Flatcar nodes, collecting GPU utilization, CPU usage, memory usage, plus HPC job-level metrics.
Grafana: Provides a unified dashboard for real-time HPC usage across both local and AWS resources, letting HPC operators see queue lengths, instance costs, and job durations.
5. Cost and Usage Controls
When a burst HPC job finishes, the Auto Scaling group reduces the number of AWS-based Flatcar nodes, ensuring BioCompute Labs only pays for the actual usage hours. They also use AWS Spot Instances for some non-time-critical workloads, further reducing costs.
Case Study Example: A wave of new jobs arrives Monday morning from a collaborative drug screening pipeline. The HPC queue spikes, automatically spins up 20 additional AWS GPU nodes. By Wednesday night, the queue empties, and the cluster scales down. Over the course of these 72 hours, the incremental cost is far lower than hosting 20 additional on-prem GPUs year-round.
Challenges Encountered
Latency vs. InfiniBand Speeds
On-prem HPC often leverages InfiniBand for low-latency communications, crucial for certain tightly-coupled MPI (Message Passing Interface) tasks. Sending those tasks to AWS with typical Ethernet-based networking introduces higher latency.
Solution:
Reserve certain HPC tasks (like multi-node molecular dynamics) for on-prem nodes.
Offload tasks that are more embarrassingly parallel or loosely coupled to the cloud.
Data Transfer Bottlenecks
Some tasks generate gigabytes of checkpoint or intermediate data. If these tasks are offloaded to AWS, uploading or downloading large volumes can be slow or expensive. They mitigate:
S3 for storing intermediate outputs.
AWS DataSync or dedicated lines for large data moves.
A caching approach to avoid repeated data transfers for commonly used reference datasets.
Containerizing Legacy HPC Apps
Older HPC codes might rely on environment modules or specific library versions. Containerization often required rewriting or packaging these dependencies. The firm overcame this by investing developer time to produce Docker images and test them extensively in staging.
Security and Compliance
Working with proprietary genomic data and sensitive IP means thorough encryption in transit and at rest. The on-prem HPC environment was well-defined. For AWS nodes, they used:
KMS (AWS Key Management Service) or external Vault to protect secrets.
VPC with private subnets, restricting outbound traffic.
Strict ephemeral usage policies to ensure no data remains on cloud instances after the job completes.
Case Study Outcome
Performance Gains
When HPC usage spikes, the firm no longer leaves crucial workloads waiting in a queue for days. They automatically spin up 10–20 GPU or CPU nodes on AWS Flatcar, improving total throughput by 30–40% during peak times.
Example: A multi-run protein folding simulation that once took 5 days to queue and finish locally can now be done in 2 days by offloading half of the tasks. This accelerated insight often equates to real-world breakthroughs: faster drug candidate identification or more timely academic publications.
Cost Optimization
Right-Sized On-Prem: The local HPC cluster is sized for average usage, not worst-case peaks. This avoids the large capital expense of building an entire second cluster.
Spot Instances: Where job priority is lower, they exploit AWS spot pricing, cutting cloud HPC costs by 60–70% during favorable times.
Pay-As-You-Go: The AWS extension is ephemeral. Once HPC demand subsides, no further spending occurs.
Agile Research Workflow
Researchers once had to schedule HPC time weeks in advance. Now, if an urgent partner project arises, the HPC team can quickly spin up extra capacity. This agility fosters better collaborations and can attract more grants or partnerships, as BioCompute Labs can promise short turnaround times.
Final Reflections: Balancing On-Prem HPC and Cloud
Key Takeaways:
Hybrid HPC Architecture: By combining on-prem GPU clusters with ephemeral Flatcar Container Linux nodes in AWS, BioCompute Labs achieves elasticity while preserving the performance benefits of local HPC for latency-sensitive tasks.
Containerization: HPC apps benefit from containers, especially if well-packaged with GPU support. This allows the same images to run locally or on the cloud, easing environment consistency.
Automation: Slurm or a custom pipeline triggers the cloud-based expansions, meaning no manual toggling is required to handle queue surges.
Monitoring & Observability: A robust telemetry strategy is crucial for debugging throughput or data bottlenecks. Tools like Prometheus and Grafana unify the HPC viewpoint.
Future Outlook
BioCompute Labs aims to expand this approach:
Multi-Cloud: Evaluate additional GPU offerings in GCP or Azure if it proves cost-competitive.
Advanced Scheduling: Possibly integrate Kubernetes for HPC container orchestration, bridging on-prem and cloud for even deeper workload synergy.
Spot Market Strategies: Fine-tune the HPC pipeline to automatically pick the cheapest available spot instance type.
eBPF Observability: Investigate advanced eBPF-based performance metrics to capture fine-grained HPC container stats on Flatcar nodes.
In short, for biotech HPC labs wrestling with large data sets, GPU demands, and cost constraints, a hybrid HPC model combining local clusters with ephemeral Flatcar nodes in the cloud can yield best-of-both-worlds outcomes. The synergy of on-prem speed and cloud elasticity ensures that each HPC workload runs in the most optimal environment, enabling timely scientific breakthroughs without ballooning operational expenses.