AI in Genomics

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  1. AI in Genomics: Server Configuration

This article details the server configuration required for running Artificial Intelligence (AI) workloads applied to genomic data. It is aimed at system administrators and bioinformaticians new to deploying these systems within our infrastructure. We will cover hardware, software, and key considerations for a successful deployment.

Introduction

The application of AI, particularly Machine Learning and Deep Learning, to genomics is rapidly expanding. Tasks such as Genome Assembly, Variant Calling, Gene Expression Analysis, and Protein Structure Prediction are increasingly relying on computationally intensive algorithms. This necessitates robust and scalable server infrastructure. This document outlines the recommended specifications for building such a system. Understanding Big Data concepts is crucial for managing genomic datasets.

Hardware Requirements

The hardware forms the foundation of any AI-driven genomics pipeline. The following table details the recommended specifications for a base server. These specifications can be scaled up depending on the size and complexity of the datasets and models used. Consider using a Rack Server for optimal density.

Component Specification
CPU Dual Intel Xeon Gold 6338 (32 cores per CPU, 64 total) or AMD EPYC 7763 (64 cores)
RAM 512 GB DDR4 ECC Registered RAM (minimum), 1TB recommended
Storage (OS & Software) 1 TB NVMe SSD
Storage (Data) 10 TB+ NVMe SSD RAID 0 or RAID 10 (depending on performance/redundancy needs) or high-performance Network Attached Storage (NAS). Consider Object Storage for very large datasets.
GPU 4 x NVIDIA A100 80GB GPUs or equivalent (e.g. AMD Instinct MI250X)
Networking 100 Gbps Ethernet or Infiniband
Power Supply Redundant 2000W Power Supplies

Software Stack

The software stack must be carefully chosen to support the AI frameworks and genomic tools. We standardize on a Linux distribution for server deployments. See our Linux Server Setup guide for details.

Operating System

  • Ubuntu Server 22.04 LTS (Recommended)
  • CentOS Stream 9

AI Frameworks

Genomic Tools

  • BWA (Burrows-Wheeler Aligner)
  • SAMtools (Sequence Alignment/Map Tools)
  • GATK (Genome Analysis Toolkit)
  • VCFtools (Variant Calling Format Tools)

Containerization

  • Docker – for packaging and deploying applications.
  • Kubernetes – for orchestrating containerized workloads (recommended for large-scale deployments).

Storage Configuration Details

Choosing the right storage solution is critical. Genomic data is often very large and requires high throughput. Here’s a more detailed breakdown of storage considerations:

Storage Type Use Case Capacity Performance
NVMe SSD (RAID 0) Active data processing, model training, temporary files 2-10 TB Very High (Read/Write)
NVMe SSD (RAID 10) Critical data storage, redundancy 10+ TB High (Read/Write) with redundancy
Network Attached Storage (NAS) Long-term data archiving, large datasets 50+ TB Moderate to High (depending on NAS configuration)
Object Storage (e.g., S3) Archival, disaster recovery, large-scale data sharing 100+ TB Moderate (Read/Write)

It's recommended to implement a tiered storage approach, leveraging the speed of NVMe SSDs for active workloads and the cost-effectiveness of NAS or Object Storage for long-term archiving. Review the Data Backup Policy before implementation.

Networking Considerations

High-bandwidth, low-latency networking is essential for transferring large genomic datasets between servers and storage systems.

Network Component Specification
Network Interface Cards (NICs) Dual 100 Gbps Ethernet or Infiniband
Switch 100 Gbps Ethernet Switch or Infiniband Switch
Interconnect Fiber Optic Cables (OM4 or better)
Network Protocol RDMA over Converged Ethernet (RoCE) for low-latency communication

Proper network configuration, including VLAN segmentation and quality of service (QoS) settings, is crucial for ensuring optimal performance.

Monitoring and Management

Continuous monitoring and management are vital for maintaining the health and performance of the server infrastructure. Utilize our standard Server Monitoring Tools such as Prometheus and Grafana. Regularly review System Logs to identify and resolve potential issues. Automated alerts should be configured to notify administrators of critical events.


Intel-Based Server Configurations

Configuration Specifications Benchmark
Core i7-6700K/7700 Server 64 GB DDR4, NVMe SSD 2 x 512 GB CPU Benchmark: 8046
Core i7-8700 Server 64 GB DDR4, NVMe SSD 2x1 TB CPU Benchmark: 13124
Core i9-9900K Server 128 GB DDR4, NVMe SSD 2 x 1 TB CPU Benchmark: 49969
Core i9-13900 Server (64GB) 64 GB RAM, 2x2 TB NVMe SSD
Core i9-13900 Server (128GB) 128 GB RAM, 2x2 TB NVMe SSD
Core i5-13500 Server (64GB) 64 GB RAM, 2x500 GB NVMe SSD
Core i5-13500 Server (128GB) 128 GB RAM, 2x500 GB NVMe SSD
Core i5-13500 Workstation 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000

AMD-Based Server Configurations

Configuration Specifications Benchmark
Ryzen 5 3600 Server 64 GB RAM, 2x480 GB NVMe CPU Benchmark: 17849
Ryzen 7 7700 Server 64 GB DDR5 RAM, 2x1 TB NVMe CPU Benchmark: 35224
Ryzen 9 5950X Server 128 GB RAM, 2x4 TB NVMe CPU Benchmark: 46045
Ryzen 9 7950X Server 128 GB DDR5 ECC, 2x2 TB NVMe CPU Benchmark: 63561
EPYC 7502P Server (128GB/1TB) 128 GB RAM, 1 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (128GB/2TB) 128 GB RAM, 2 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (128GB/4TB) 128 GB RAM, 2x2 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (256GB/1TB) 256 GB RAM, 1 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (256GB/4TB) 256 GB RAM, 2x2 TB NVMe CPU Benchmark: 48021
EPYC 9454P Server 256 GB RAM, 2x2 TB NVMe

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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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