Data & HPC

Vision & Challenges

Our Vision

To democratize access to high-performance computing resources, enabling researchers and innovators to tackle the most challenging computational problems without the burden of infrastructure investment.

Addressing Key Challenges

Computational Complexity

Modern research problems require massive computational power that exceeds the capabilities of standard systems.

Data Scale

Managing and processing petabyte-scale datasets demands specialized storage and processing infrastructure.

Cost Barriers

Building and maintaining HPC infrastructure requires significant capital investment and specialized expertise.

Technology Evolution

Rapid advancement in computing technology makes it difficult for individual organizations to stay current.

Use Cases & Applications

Artificial Intelligence & Machine Learning

Deep learning model training
Neural network optimization
Natural language processing
Computer vision applications
Reinforcement learning
Large language models

Computational Biology & Genomics

Genome sequencing and assembly
Protein structure prediction
Drug discovery simulations
Molecular dynamics
Population genetics analysis
Systems biology modeling

Data Science & Analytics

Big data processing
Statistical modeling
Predictive analytics
Time series analysis
Data mining and pattern recognition
Real-time analytics

Scientific Computing

Climate modeling and simulation
Computational fluid dynamics
Materials science simulations
Quantum chemistry calculations
Astrophysical simulations
Financial modeling

Engineering & Design

Finite element analysis (FEA)
Computational electromagnetics
Structural optimization
Multi-physics simulations
CAD/CAM operations
Digital twin development

Image & Signal Processing

Medical imaging analysis
Satellite image processing
Video rendering and encoding
3D reconstruction
Signal filtering and enhancement
Pattern recognition

Computing Infrastructure

Computing Resources

CPU Cores: 10,000+ cores
GPU Accelerators: NVIDIA A100, H100
Memory: 100+ TB RAM
Performance: 5+ petaFLOPS
Architecture: Hybrid CPU/GPU clusters

Storage Systems

High-Speed Storage: 10+ PB
Archive Storage: 50+ PB
IOPS: Millions of operations/sec
Bandwidth: 100+ GB/s throughput
Backup: Automated redundancy

Networking

Interconnect: InfiniBand HDR
Bandwidth: 200 Gb/s
Latency: Sub-microsecond
External: 100 Gb/s internet
Network: RENATER research network

Software & Tools

Job Scheduler: SLURM
Containers: Singularity, Docker
Frameworks: TensorFlow, PyTorch, etc.
Compilers: Intel, GNU, CUDA
Libraries: MKL, cuDNN, OpenMPI

Services & Support

Access Models

On-demand compute hours
Reserved capacity allocations
Dedicated node reservations
Priority queue access
Burst computing capability

Technical Support

Code optimization consulting
Parallelization assistance
Performance tuning
Software installation support
Debugging and troubleshooting

Training & Education

HPC fundamentals courses
Parallel programming workshops
GPU computing training
Best practices seminars
One-on-one consultations

Roadmap & Future Development

Q1 2026

Phase 1 - Current

5 petaFLOPS computing capacity operational

GPU cluster with NVIDIA A100 accelerators

Q3 2026

Phase 2 - Expansion

Double computing capacity to 10 petaFLOPS

Next-generation GPU integration (H100/H200)

Enhanced storage to 100 PB

Q1 2027

Phase 3 - Quantum Integration

Quantum computing testbed

Hybrid classical-quantum workflows

Advanced AI accelerators

2028

Phase 4 - Exascale

Target: 1 exaFLOPS capability

Energy-efficient architecture

AI-optimized infrastructure