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ALPCRUN.CH

Welcome to the official documentation for ALPCRUN.CH, a high-performance computing (HPC) cluster grid orchestrator and middleware designed for distributed task processing at scale.

What is ALPCRUN.CH?

ALPCRUN.CH enables efficient distributed computation through a queue- and cache-based architecture. Built on top of Kubernetes and gRPC, it provides a robust framework for submitting computational workloads, managing distributed workers, and collecting results.

Key Features

  • Bidirectional Streaming: Efficient task and result streaming using gRPC
  • Priority-Based Scheduling: Control task execution order with flexible priority levels
  • Distributed Caching: Share data across workers with versioned, TTL-based caching
  • Session Management: Isolated workload contexts with configurable resources
  • Dead Letter Queues: Automatic handling of failed tasks for inspection and retry
  • Scalable Architecture: Horizontally scalable services designed for flexible cloud deployment
  • Flow Control: Built-in backpressure mechanisms to prevent system overload

Use Cases

ALPCRUN.CH is ideal for:

Monte Carlo Simulations: Distribute statistical computations across multiple workers

  • Price exotic derivatives and complex financial instruments (options, swaps, structured products)
  • Calculate Value at Risk (VaR) and Conditional Value at Risk (CVaR) for portfolio risk management
  • Perform credit risk modeling and default probability estimation
  • Run reliability analysis and failure probability calculations for engineering systems
  • Compute high-dimensional integrals and solve stochastic differential equations
  • Simulate particle physics interactions and quantum system behaviors

Parameter Sweeps: Run thousands of variations of computational models in parallel

  • Hyperparameter optimization for machine learning models (grid search, random search, Bayesian optimization)
  • Sensitivity analysis for engineering designs (varying material properties, dimensions, loads)
  • Climate model ensembles with different initial conditions and physical parameters
  • A/B testing simulation for product features and business scenarios
  • Optimization of manufacturing processes by testing process parameter combinations
  • Computational fluid dynamics (CFD) with varying boundary conditions and mesh configurations

Batch Processing: Process large datasets with automatic task distribution

  • Video transcoding and media processing pipelines (format conversion, thumbnail generation)
  • Large-scale ETL operations for data warehouses and analytics platforms
  • Document processing and OCR for digitization projects
  • Log analysis and aggregation across distributed systems
  • Batch rendering for 3D graphics, animations, and visual effects
  • Genomic sequence alignment and bioinformatics data processing

GPU Computing: Schedule GPU-accelerated workloads across heterogeneous clusters

  • Enhanced Monte Carlo simulations at massive parallelism
  • Deep learning model training with distributed data parallelism
  • Molecular dynamics simulations for drug design and materials science
  • Real-time ray tracing and photorealistic rendering
  • Computer vision tasks (object detection, image segmentation, style transfer)
  • Accelerated finite element analysis (FEA) for structural simulations

Scientific Computing: High-throughput computing for research applications

  • Astrophysics simulations (N-body problems, galaxy formation, gravitational waves)
  • Weather forecasting and atmospheric modeling
  • Protein folding simulations and molecular docking studies
  • Epidemiological modeling and disease spread analysis
  • Seismic data processing and earthquake simulation
  • Materials science calculations (density functional theory, crystal structure prediction)

Defense & Aerospace: Mission-critical parallel computing for defense applications

  • Ballistic trajectory calculations and missile intercept scenario analysis
  • Radar and sonar signal processing for target detection and tracking
  • Combat simulation and war gaming with thousands of tactical variations
  • Blast and weapon effects modeling under varying environmental conditions
  • Satellite orbit propagation and collision avoidance for space operations
  • Cryptanalysis and security vulnerability testing across defense systems

Quick Start

Ready to get started? Follow our Getting Started guide to:

  1. Set up the ALPCRUN.CH services
  2. Write your first client application
  3. Submit and process workloads
  4. Collect and analyze results

Architecture

ALPCRUN.CH uses a traditional 3-tier architecture:

  • Clients submitting workloads and retrieving results
  • Data Layer with the queues and caches, scheduling workloads on the workers and managing priorization, state, and data transfer
  • Workers doing the hard work of running the user's services, crunching on tasks and delivering results back to the data layer and clients

The ALPCRUN.CH middlware itself consists of three core services that users interact with:

  • Queue Manager: Central service for session management and task distribution and priorization
  • Central Cache: Shared data storage with versioning and TTL support
  • Node Cache: Local pull-through cache for improved performance

Learn more in the Architecture Overview.

Documentation Structure

Support

ALPCRUN.CH is developed and maintained by Lime Labs GmbH. For questions or issues, please send a mail to support@alpcrun.ch.

Ready to dive in? Start with the Getting Started guide.