Scalable Parallel Computing Lab, SPCL @ ETH Zurich Courses

Swing: Short-cutting Rings for Higher Bandwidth Allreduce

Optimize allreduce performance on torus networks with Swing algorithm. Improve bandwidth by reducing distance between communicating nodes, outperforming existing methods by up to 3x.

• YouTube
• 26 minutes
• On-Demand
• Free Video

HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers

Explore higher-order dynamic graph representation learning with efficient Transformers for improved link prediction in rapidly changing graphs. Learn about HOT model's architecture and performance.

• YouTube
• 20 minutes
• On-Demand
• Free Video

VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores

Explore VENOM, a vectorized N:M format for sparse tensor cores. Learn about high-performance sparse libraries and second-order pruning techniques for efficient deep learning model sparsification.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Motif Prediction with Graph Neural Networks

Explore advanced graph mining techniques using Graph Neural Networks for motif prediction, enhancing link prediction capabilities and enabling higher-order network analysis.

• YouTube
• 23 minutes
• On-Demand
• Free Video

The Digital Revolution of Earth System Modelling

Explore Earth system modeling's evolution: quiet, digital, and machine learning revolutions. Discover past developments, current challenges, and future prospects in weather prediction and climate simulation.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Video

Capturing Computation with Algorithmic Alignment

Explore algorithmic alignment in neural networks, its implications for architecture design, and its potential for building future intelligent systems.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Co-design Hardware and Algorithm for Vector Search

Explore hardware-algorithm co-design for efficient vector search on FPGAs, enhancing performance in large-scale information retrieval and machine learning systems.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Fortran Optimization for Accelerator Devices - Data-Centric Representation

Explore data-centric optimization techniques for Fortran code on accelerator devices, enhancing high-performance computing capabilities.

• YouTube
• 21 minutes
• On-Demand
• Free Video

EtherNET: The Present and Future of Datacenter and Supercomputer Interconnects

Explore the role of Ethernet in modern datacenters and supercomputers, examining its current state and future prospects in high-performance computing environments.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Can I Cook a 5 o'clock Compiler Cake and Eat It at 2? - Optimizing Compilers for High-Performance Computing

Explore ML-enhanced compilers and domain-specific code generators in high-performance computing. Examine the programmability/performance dilemma and potential solutions for future compiler development.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

AI-Driven Performance Metaprogramming - Embedding Spaces for Program Analysis and Optimization

Explore AI-driven performance metaprogramming using embedding spaces for programs. Learn to assess, analyze, and improve program performance through advanced AI methods.

• YouTube
• 45 minutes
• On-Demand
• Free Video

HammingMesh: A Network Topology for Large-Scale Deep Learning

Explore a novel network topology for large-scale deep learning systems, enhancing data movement efficiency and job scheduling flexibility for future AI training infrastructure.

• YouTube
• 34 minutes
• On-Demand
• Free Video

The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads

Explore cutting-edge graph database technology scaling to 100,000+ cores. Learn about GDI, a new interface for high-performance, scalable graph processing in distributed memory systems.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Scalable Graph Machine Learning - Accelerating GNN Computations

Explore scalable graph machine learning techniques, focusing on Graph Neural Networks. Learn about efficient methods for training and inference, including graph sampling and partitioning for large-scale graphs.

• YouTube
• 1 hour
• On-Demand
• Free Video

Heterogeneous Multi-Core Systems for Efficient Edge Machine Learning

Explore heterogeneous multi-core systems for efficient embedded ML, addressing challenges in core optimization, workload mapping, and data sharing. Gain insights from KULeuven MICAS research.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Video