483 citations · 3k across the 177 of their papers we have counts for
204 papers
HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills
Yakup Koray Budanaz, Lorenzo Paleari, Marcin Copik +7
Coding agents are increasingly used to optimize code, but existing benchmarks fix one accelerator, programming model or harness, so they cannot isolate what each component contribu…
Fetch Me If You Can: A Comprehensive Analysis of Fetch Metadata
Pedro Bernardo, Alex Ploner, Marco Squarcina +2
Effective server-side defenses against cross-site attacks depend on understanding the context in which browser requests are generated. However, servers often lack the information n…
PCM: Programmable Congestion Management for Terabit RDMA NICs
Mikhail Khalilov, Timo Schneider, Tommaso Bonato +10
Datacenter congestion control (CC) and load balancing (LB) evolve rapidly as workload demands change, but deployment depends on RDMA hardware and transport interfaces. Emerging pro…
What Bloats Your Floats? Right-Sizing Numerical Precision for Scientific Computing
Pratyai Mazumder, Alexandru Calotoiu, Marcin Copik +3
While FP64 (binary64) remains the standard representation of real numbers in scientific computing, hardware trends increasingly favor low-precision formats optimized for AI workloa…
Causally Fair Generation with Large Language Models
Patrik Okanovic, Torsten Hoefler, Drago Plecko
Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape consequential decisions, raising conce…
Which LLM to pick? Online Active Model Selection for Large Language Models
Alessandro Turrin, Patrik Okanovic, Torsten Hoefler +1
Large Language Models (LLMs) are increasingly applied to process streaming data, with practitioners relying on benchmarks to select the best model even though these signals only ap…
WUSH-KV: KV Cache Quantization with Data-Adaptive Transforms
Jiale Chen, Vage Egiazarian, Eldar Kurtić +2
KV cache memory and bandwidth costs grow with context length and batch size, which limits efficient long-context inference. To address this bottleneck, we introduce WUSH-KV for low…
FP64 Is All You Want, INT8 Is All You Need, FP4/6/8 Is All You Have
Pratyai Mazumder, Alexandru Calotoiu, Torsten Hoefler
Ozaki scheme II emulates FP64 matrix products with INT8 ones through residues modulo pairwise coprime moduli, and variants for FP8 and FP4 have followed. We treat these schemes as…
Hardware-Aware Features for CUTLASS Kernel Selection
Shriram Chandran, Dominic Rinderer, Yakup Budanaz +3
GPU libraries such as CUTLASS expose tens of thousands of semantically equivalent kernels for a single operation, making exhaustive autotuning expensive and execution-free selectio…
Gauss What You Need: Compact Gaussian Splatting Across Scene Scales
Afif Boudaoud, Jiayi Liu, Alexandru Calotoiu +1
3D Gaussian Splatting reconstructs a scene as a collection of Gaussian primitives from a set of posed photographs called the capture. The number of primitives used to represent the…
The Canonical Parallel Form as a Substrate for Parallelizing Compilers and Agentic Optimizers
Yakup Koray Budanaz, Pratyai Mazumder, Alexandru Calotoiu +1
Imperative code fixes an execution order the computation does not require, and a parallelizing compiler must prove which parts of that order it can remove. We introduce the Canonic…
Quantum computers will not be that different: A blueprint for quantum computer architecture at scale
Torsten Hoefler, Matthias Troyer
Quantum computers are technologically novel and unusual, but at system scale they should be engineered using many of the same principles that govern classical heterogeneous acceler…
Extreme-Scale Linear-Scaling Kohn-Sham DFT at 100 Million Atoms: Bridging Quantum Simulations and Experiments
Qimen Xu, Yu Zhang, Dixing Ni +11
Kohn-Sham density functional theory (DFT) remains the workhorse of ab initio materials simulation, yet cubic computational and quadratic memory scaling have confined calculations t…
Process Reward Agents for Steering Knowledge-Intensive Reasoning
Jiwoong Sohn, Tomasz Sternal, Kenneth Styppa +2
Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require sy…
When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
Boqian Wu, Qiao Xiao, Patrik Okanovic +6
Scaling laws for dense LLMs under infinite data are well explored, but how sparsity interacts with limited data is not. In this work, we study sparse training in data-constrained r…
Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling
Qiao Xiao, Boqian Wu, Patrik Okanovic +6
Dynamic Sparse Training (DST) offers a promising paradigm for improving the training and inference efficiency of deep neural networks; however, we find that in large language model…
Confounder Detection via Treatment Intent: A New Observational Study Design
Drago Plecko, Patrik Okanovic, Torsten Hoefler +1
Understanding the effects of interventions is central to scientific progress, with randomized controlled trials (RCTs) regarded as the gold standard for causal inference in many ap…
Large Language Model Selection with Limited Annotations
Yavuz Durmazkeser, Patrik Okanovic, Andreas Kirsch +2
Choosing a Large Language Model (LLM) for a given task requires comparing many strong candidates, yet standard evaluation relies on costly annotations over fixed evaluation sets. T…
Grid Games: The Power of Multiple Grids for Quantizing Large Language Models
Vage Egiazarian, Erik Schultheis, Andrei Panferov +3
A major recent advance in quantization is given by microscaled 4-bit formats such as NVFP4 and MXFP4, quantizing values into small groups sharing a scale, assuming a fixed floating…
ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations
Afif Boudaoud, Lisa Gaedke-Merzhäuser, Alexandros Nikolaos Ziogas +6
Spatio-temporal Bayesian inference drives environmental and health sciences using latent Gaussian models. Integrated Nested Laplace Approximations (INLA) enable inference for these…
Resilient AI Supercomputer Networking using MRC and SRv6
Joao Araujo, Alex Chow, Mark Handley +47
Tail latency dominates the performance of synchronous pretraining jobs when running at very large scales. We describe a three-pronged approach: (1) a new RDMA-based transport proto…
Scaling Laws of Global Weather Models
Yuejiang Yu, Langwen Huang, Alexandru Calotoiu +1
Data-driven models are revolutionizing weather forecasting. To optimize training efficiency and model performance, this paper analyzes empirical scaling laws within this domain. We…
Design in Tiles: Automating GEMM Deployment on Tile-Based Many-PE Accelerators
Aofeng Shen, Chi Zhang, Yakup Budanaz +4
Tile-based many-Processing Element (PE) accelerators can achieve competitive performance on General Matrix Multiplication (GEMM), but they are extremely hard to program, as their o…
WUSH: Near-Optimal Adaptive Transforms for LLM Quantization
Jiale Chen, Vage Egiazarian, Roberto L. Castro +2
Quantizing LLM weights and activations is a standard approach for efficient deployment, but a few extreme outliers can stretch the dynamic range and amplify low-bit quantization er…