Torsten Hoefler

research

∙ 09/16/2023

Earth Virtualization Engines – A Technical Perspective

Participants of the Berlin Summit on Earth Virtualization Engines (EVEs)...

0 Torsten Hoefler, et al. ∙

research

∙ 09/07/2023

OSMOSIS: Enabling Multi-Tenancy in Datacenter SmartNICs

Multi-tenancy is essential for unleashing SmartNIC's potential in datace...

0 Mikhail Khalilov, et al. ∙

research

∙ 08/23/2023

Cached Operator Reordering: A Unified View for Fast GNN Training

Graph Neural Networks (GNNs) are a powerful tool for handling structured...

0 Julia Bazinska, et al. ∙

research

∙ 08/18/2023

Graph of Thoughts: Solving Elaborate Problems with Large Language Models

We introduce Graph of Thoughts (GoT): a framework that advances promptin...

0 Maciej Besta, et al. ∙

research

∙ 07/17/2023

Differentiable Transportation Pruning

Deep learning algorithms are increasingly employed at the edge. However,...

0 Yunqiang Li, et al. ∙

research

∙ 07/17/2023

Maximum Flows in Parametric Graph Templates

Execution graphs of parallel loop programs exhibit a nested, repeating s...

0 Tal Ben-Nun, et al. ∙

research

∙ 07/02/2023

Disentangling Hype from Practicality: On Realistically Achieving Quantum Advantage

Quantum computers offer a new paradigm of computing with the potential t...

0 Torsten Hoefler, et al. ∙

research

∙ 06/28/2023

FuzzyFlow: Leveraging Dataflow To Find and Squash Program Optimization Bugs

The current hardware landscape and application scale is driving performa...

0 Philipp Schaad, et al. ∙

research

∙ 06/19/2023

Co-design Hardware and Algorithm for Vector Search

Vector search has emerged as the foundation for large-scale information ...

0 Wenqi Jiang, et al. ∙

research

∙ 06/05/2023

SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Recent advances in large language model (LLM) pretraining have led to hi...

0 Tim Dettmers, et al. ∙

research

∙ 06/05/2023

Streaming Task Graph Scheduling for Dataflow Architectures

Dataflow devices represent an avenue towards saving the control and data...

0 Tiziano De Matteis, et al. ∙

research

∙ 06/01/2023

Bridging Control-Centric and Data-Centric Optimization

With the rise of specialized hardware and new programming languages, cod...

0 Tal Ben-Nun, et al. ∙

research

∙ 05/18/2023

The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores

Graph databases (GDBs) are crucial in academic and industry applications...

0 Maciej Besta, et al. ∙

research

∙ 05/15/2023

FMI: Fast and Cheap Message Passing for Serverless Functions

Serverless functions provide elastic scaling and a fine-grained billing ...

0 Marcin Copik, et al. ∙

research

∙ 05/09/2023

Sparse Stream Semantic Registers: A Lightweight ISA Extension Accelerating General Sparse Linear Algebra

Sparse linear algebra is crucial in many application domains, but challe...

0 Paul Scheffler, et al. ∙

research

∙ 05/09/2023

A High-performance, Energy-efficient Modular DMA Engine Architecture

Data transfers are essential in today's computing systems as latency and...

0 Thomas Benz, et al. ∙

research

∙ 05/08/2023

ASDL: A Unified Interface for Gradient Preconditioning in PyTorch

Gradient preconditioning is a key technique to integrate the second-orde...

0 Kazuki Osawa, et al. ∙

research

∙ 04/15/2023

STen: Productive and Efficient Sparsity in PyTorch

As deep learning models grow, sparsity is becoming an increasingly criti...

0 Andrei Ivanov, et al. ∙

research

∙ 03/14/2023

Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization

Performance optimization is an increasingly challenging but often repeti...

0 Lukas Trümper, et al. ∙

research

∙ 02/14/2023

PolarStar: Expanding the Scalability Horizon of Diameter-3 Networks

In this paper, we present PolarStar, a novel family of diameter-3 networ...

0 Kartik Lakhotia, et al. ∙

research

∙ 01/17/2023

AutoDDL: Automatic Distributed Deep Learning with Asymptotically Optimal Communication

Recent advances in deep learning base on growing model sizes and the nec...

0 Jinfan Chen, et al. ∙

research

∙ 01/06/2023

Myths and Legends in High-Performance Computing

In this humorous and thought provoking article, we discuss certain myths...

0 Satoshi Matsuoka, et al. ∙

research

∙ 01/03/2023

A Theory of I/O-Efficient Sparse Neural Network Inference

As the accuracy of machine learning models increases at a fast rate, so ...

0 Niels Gleinig, et al. ∙

research

∙ 12/18/2022

Efficient RDMA Communication Protocols

Developers of networked systems often work with low-level RDMA libraries...

0 Konstantin Taranov, et al. ∙

research

∙ 11/25/2022

PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices

Pipeline parallelism enables efficient training of Large Language Models...

0 Kazuki Osawa, et al. ∙

research

∙ 11/25/2022

HexaMesh: Scaling to Hundreds of Chiplets with an Optimized Chiplet Arrangement

2.5D integration is an important technique to tackle the growing cost of...

0 Patrick Iff, et al. ∙

research

∙ 11/25/2022

Sparse Hamming Graph: A Customizable Network-on-Chip Topology

Chips with hundreds to thousands of cores require scalable networks-on-c...

0 Patrick Iff, et al. ∙

research

∙ 11/24/2022

Spatial Mixture-of-Experts

Many data have an underlying dependence on spatial location; it may be w...

0 Nikoli Dryden, et al. ∙

research

∙ 11/14/2022

Assessing requirements to scale to practical quantum advantage

While quantum computers promise to solve some scientifically and commerc...

0 Michael E. Beverland, et al. ∙

research

∙ 10/31/2022

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Generative Pre-trained Transformer (GPT) models set themselves apart thr...

0 Elias Frantar, et al. ∙

research

∙ 10/27/2022

Noise in the Clouds: Influence of Network Performance Variability on Application Scalability

Cloud computing represents an appealing opportunity for cost-effective d...

0 Daniele De Sensi, et al. ∙

research

∙ 10/22/2022

Compressing multidimensional weather and climate data into neural networks

Weather and climate simulations produce petabytes of high-resolution dat...

10 Langwen Huang, et al. ∙

research

∙ 09/20/2022

Neural Graph Databases

Graph databases (GDBs) enable processing and analysis of unstructured, c...

65 Maciej Besta, et al. ∙

research

∙ 09/19/2022

Temporal Vectorization: A Compiler Approach to Automatic Multi-Pumping

The multi-pumping resource sharing technique can overcome the limitation...

0 Carl-Johannes Johnsen, et al. ∙

research

∙ 09/14/2022

Efficient Quantized Sparse Matrix Operations on Tensor Cores

The exponentially growing model size drives the continued success of dee...

13 Shigang Li, et al. ∙

research

∙ 09/07/2022

SAGE: Software-based Attestation for GPU Execution

With the application of machine learning to security-critical and sensit...

0 Andrei Ivanov, et al. ∙

research

∙ 09/03/2022

HammingMesh: A Network Topology for Large-Scale Deep Learning

Numerous microarchitectural optimizations unlocked tremendous processing...

25 Torsten Hoefler, et al. ∙

research

∙ 08/24/2022

ProbGraph: High-Performance and High-Accuracy Graph Mining with Probabilistic Set Representations

Important graph mining problems such as Clustering are computationally d...

0 Maciej Besta, et al. ∙

research

∙ 08/02/2022

PolarFly: A Cost-Effective and Flexible Low-Diameter Topology

In this paper we present PolarFly, a diameter-2 network topology based o...

0 Kartik Lakhotia, et al. ∙

research

∙ 06/29/2022

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast

Post-processing ensemble prediction systems can improve weather forecast...

14 Saleh Ashkboos, et al. ∙

research

∙ 06/21/2022

Boosting Performance Optimization with Interactive Data Movement Visualization

Optimizing application performance in today's hardware architecture land...

0 Philipp Schaad, et al. ∙

research

∙ 06/20/2022

Building Blocks for Network-Accelerated Distributed File Systems

High-performance clusters and datacenters pose increasingly demanding re...

0 Salvatore Di Girolamo, et al. ∙

research

∙ 06/16/2022

Deinsum: Practically I/O Optimal Multilinear Algebra

Multilinear algebra kernel performance on modern massively-parallel syst...

0 Alexandros Nikolaos Ziogas, et al. ∙

research

∙ 05/19/2022

Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis

Graph neural networks (GNNs) are among the most powerful tools in deep l...

92 Maciej Besta, et al. ∙

research

∙ 05/10/2022

The spatial computer: A model for energy-efficient parallel computation

We present a new parallel model of computation suitable for spatial arch...

0 Lukas Gianinazzi, et al. ∙

research

∙ 05/09/2022

Productive Performance Engineering for Weather and Climate Modeling with Python

Earth system models are developed with a tight coupling to target hardwa...

0 Tal Ben-Nun, et al. ∙

research

∙ 04/13/2022

Fast Arbitrary Precision Floating Point on FPGA

Numerical codes that require arbitrary precision floating point (APFP) n...

0 Johannes de Fine Licht, et al. ∙

research

∙ 03/28/2022

FaasKeeper: a Blueprint for Serverless Services

FaaS (Function-as-a-Service) brought a fundamental shift into cloud comp...

0 Marcin Copik, et al. ∙

research

∙ 02/28/2022

Asynchronous Distributed-Memory Triangle Counting and LCC with RMA Caching

Triangle count and local clustering coefficient are two core metrics for...

0 András Strausz, et al. ∙

research

∙ 02/16/2022

NeVerMore: Exploiting RDMA Mistakes in NVMe-oF Storage Applications

This paper presents a security analysis of the InfiniBand architecture, ...

0 Konstantin Taranov, et al. ∙

Torsten Hoefler

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