4-10 June 2017
MEDILS in Split, Croatia
Europe/Zurich timezone

Scientific Programme

Efficient Parallel Processing of Future Scientific Data

Introduction

  • Future scientific data processing: challenges in HEP and other sciences, commonalities and differences.
  • The prime role of  software in modern big science.
  • Parallelism and asynchronism: computation and I/O.
  • Evolution of hardware and platforms, consequences on data analysis procedures and tools.

Track 1: Technologies and Platforms

Introduction to Efficient Computing

  • The evolution of computing hardware and what it means in practice
  • The seven dimensions of performance
  • Controlling and benchmarking your computer and software
  • Software that scales with the hardware
  • Advanced performance tuning in hardware

Intermediate Concepts in Efficient Computing

  • Memory architectures, hardware caching and NUMA
  • Scaling out: Big Data – Big Hardware
  • The role of compilers and VMs
  • A brief look at accelerators and heterogeneity

Data Oriented Design

  • Hardware vectorization in detail – theory vs. practice
  • Software design for vectorization and smooth data flow
  • How can compilers and other tools help?

Summary and Future Technologies Overview

  • Teaching program summary and wrap-up
  • Next-generation memory technologies and interconnect
  • Rack-sized datacenters and future computing evolution
  • Software technologies – forecasts

Track 2: Programming for concurrency and correctness

Scientific software programming: a modern approach

  • Introduction: Amdahl's law, Performance and correctness of codebases
  • Modern C++: new constructs, their advantages
  • Exploit modern architectures using Python
  • Near the hardware: the role of compilers
  • Understanding the differences and commonalities of data structures, metrics for their classification, concrete examples

Expressing Parallelism Pragmatically

  • Trivial asynchronous execution
  • Task and data decomposition
  • Threads and the thread pool model
  • In depth comparison of threads and processes, guidelines to choose the best option

Protection of Resources and Thread Safety

  • The problem of synchronization
  • Useful design principles
  • Replication, atomics, transactions and locks
  • Lock-free programming techniques
  • Functional programming style and elements of map-reduce
  • Third party libraries and high level solutions

Ensure Correctness of a Parallel Scientific Application

  • Correctness and reproducibility of a scientific result
  • Stability of results and testing: regression, physics performance, tradeoffs
  • Enforce avoiding thread unsafe constructs: focus on static analysis
  • Algorithms for detecting synchronisation pathologies: focus on the DRD and Helgrind tools
  • Elements of the GNU debugger: introduction and specific usage in the multithreaded case

Track 3: Effective I/O for Scientific Applications

Structuring data for efficient I/O

  • Pro/cons of row-column and mixed formats
  • compression and its efficiency dependencies on variable types, impact of data format
  • Data addressing : limitation of hierarchical approach, usage of flat namespaces
  • Stateful vs stateless interfaces for namespaces and I/O

Many ways to store data

  • Storage devices and their specificities
  • Data federation  
  • Parallelizing files storage
  • Introduction to the Map/Reduce pattern

Preserving Data

  • Risks of data loss and corruption
  • Data consistency (checksumming)
  • Data safety (redundancy, parity, erasure coding)

Key Ingredients to achieve effective I/O

  • Asynchronous I/O
  • I/O optimizations
  • Caching
  • Influence of data structures on I/O efficiency
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