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Compute Infrastructure

HPC Cluster Architecture

TU HPC is structured into multiple compute nodes, ensuring scalability and efficiency. The key components of the infrastructure include:

1. Compute Nodes

TU HPC consists of multiple compute nodes, categorized into different partitions:

  • Normal Partition: General-purpose nodes for CPU-based computing.
  • Protein Partition: Dedicated GPU node for high-performance computing.
  • FPLO Partition: Nodes configured for specific research applications.

Node Configuration

  • Total Compute Nodes: 10
  • CPU Architecture: x86_64
  • Processor Model: Intel(R) Xeon(R) CPU E5-2680 v2 @ 2.80GHz
  • Total CPU Cores: 20 per node (10 cores per socket, 2 sockets per node)
  • Threading: 1 thread per core
  • NUMA Nodes: 2 (Ensuring optimized memory access)

2. Job Scheduling System

  • SLURM-based workload manager for efficient job scheduling and resource allocation.
  • Supports batch and interactive job submission.
  • Users can specify CPU, GPU, memory, and time requirements for optimal usage.

3. GPU Infrastructure

  • Dedicated GPU Node: Available under the Protein Partition.
  • Supports CUDA and OpenCL-based parallel computing.
  • Optimized for deep learning, molecular dynamics, and AI research.

4. Storage & File Systems

TU HPC provides a multi-tiered storage system to support computational research:

  • Root Storage: /dev/mapper/centos-root – 50GB (OS & system files)
  • Software Storage: /dev/sdb1 – 470GB (Installed software and modules)
  • Scratch Storage: /mnt/storage0 – 1.9TB (High-speed temporary storage)
  • Home Directory: /home – 1.8TB (User personal storage)
  • Memory File System: tmpfs – 126GB (High-speed temporary memory storage)

5. Networking & Connectivity

  • High-speed interconnect for low-latency data transfer between nodes.
  • Secure SSH access for remote login.

Performance & Scalability

The HPC infrastructure at TU is designed to support high-throughput computing (HTC) and massively parallel processing (MPP). It provides:

  • Scalability to accommodate growing computational demands.
  • Resource optimization to maximize job efficiency.
  • Flexible configurations for different research requirements.

Supported Workloads

The compute infrastructure supports a wide range of research activities, including:

  • Computational Physics & Chemistry
  • Machine Learning & Deep Learning
  • Molecular Dynamics Simulations
  • Weather & Climate Modeling
  • Structural Engineering & Finite Element Analysis
  • High-Performance Data Analytics

Future Upgrades

TU HPC is continuously expanding with:

  • More GPU nodes for AI and scientific computing.
  • Increased storage capacity for big data applications.
  • Enhanced networking and security to ensure seamless operations.

For more details on how to access and utilize TU HPC resources, visit the Getting Started Guide.

Empowering research with high-performance computing! 🚀