arXiv subjects

Torsten Hoefler

Publications and source records attributed to Torsten Hoefler.

At least 19 recordsLinked to original sources

arXiv2026

Skillful and realistic precipitation forecasting with reinforced diffusion

Accurate precipitation forecasting remains elusive as physics-based models struggle to resolve mesoscale convection and machine learning often yields physically inconsistent, blurred results that consistently underestimate extreme events. We present FitzRoy, the first system that simultaneously achieves state-of-the-art predictive skill and physical realism over the continental United States. Through a reinforced diffusion framework, where a conditional discrete diffusion model is refined via reinforcement learning, FitzRoy effectively resolves the long-standing trade-off between pointwise accuracy and structural fidelity. Across lead times up to 24 hours, FitzRoy consistently matches or exceeds state-of-the-art models while preserving structural realism and capturing extreme events. Its low-latency inference further enables deployment in real-time operational settings. In case studies such as the 2025 Texas Hill Country floods and the 2026 Missouri floods, FitzRoy signals record-breaking accumulations that existing models largely missed. By bridging the gap between realism and skill, FitzRoy establishes a new frontier for high-fidelity precipitation forecasting.

cs.LG↗

arXiv2026

EBCC v0.2: Error bounded compression for weather and climate applications

As the resolution of weather and climate simulations increases, the amount of data produced is growing rapidly from hundreds of terabytes to tens of petabytes. These data volumes limit broader adoption, and their continued growth will soon exhaust available storage capacity. To address this problem, we present EBCC v0.2 (Error Bounded Climate-data Compressor). EBCC follows a two-layer compression approach: a base layer using JPEG2000 to capture the bulk of the data at high compression ratios, and a residual layer storing sparse wavelet coefficients to eliminate long-tail extreme errors efficiently. It supports three-dimensional chunks through a multicomponent transform (MCT) in the base layer. To achieve error bounded compression, we introduce error-bound-aware rate allocation in the JPEG2000 base layer. It selects coding passes to approximately satisfy the error target while optimizing the trade-off between compression ratio and mean squared error. We evaluate EBCC alongside established compression methods on benchmarks relevant to weather and climate science, including error statistics, sequential and parallel throughput tests, case studies of primitive and derived variables near a tropical cyclone, an analysis of global energy-budget closure, and a Lagrangian air-parcel trajectory simulation. EBCC either outperforms or closely follows the strongest existing baseline across these benchmarks at relative error targets ranging from 0.01% to 10%, achieving compression ratios from 15x to more than 300x. In the energy-budget-closure and Lagrangian-trajectory benchmarks, EBCC achieves more than 100x compression while keeping compression-induced errors below an assimilation-uncertainty reference derived from the ERA5 uncertainty ensemble. These results demonstrate that EBCC can produce heavily compressed weather and climate datasets suitable for downstream applications.

cs.CE↗

arXiv2026

HPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and Skills

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 contributes or show whether added skills and tools repay their cost. We present HPCAgent-Bench with 689 kernels in three tracks: loop-level optimization challenges, kernels extracted from HPC codes, and machine-learning models. The framework specifies each kernel once in NumPy, derives C and Fortran references, accepts submissions through a common C ABI or Python interface, and runs them on CPUs or GPUs, on one or multiple nodes. An external judge checks correctness on hidden fuzzed inputs and times each submission, closing paths agents use to exploit the testing system. We introduce two metrics for reporting results: \textit{intervention efficacy} measures an intervention, such as adding a tool, through paired runs on speedup, success rate and cost, and \textit{weighted token cost} reports input, cached-input and output tokens under explicit weights. Agents reach up to $4.2\times$ speedup over automatic parallelizers, and their optimizations transfer to another machine: $93$--$99\%$ of answers without device-specific code remain correct, and per-kernel speedups keep a rank correlation of $r_s\ge0.84$. Across 40 matched setups, interventions significantly change speedup only twice but token cost by up to $3.0\times$. Because token weighting decides whether a saving is significant, we state the weights and release every token count as a standard for cost reporting. We provide the first comprehensive, broad and hardened benchmark for the sound evaluation of agentic optimization, with transparent cost metrics.

cs.SE↗

arXiv2026

PCM: Programmable Congestion Management for Terabit RDMA NICs

Datacenter congestion control (CC) and load balancing (LB) evolve rapidly as workload demands change, but deployment depends on RDMA hardware and transport interfaces. Emerging programmable RDMA engines tie policy code to vendor APIs. Operators must therefore port each policy for every platform they deploy. We address this with PCM, a programming model that separates portable congestion management from platform integration on datacenter NICs. Developers write event-driven handlers that they can debug in simulation and reuse on real hardware. PCM exposes flow telemetry as signals and uses triggers to support per-event reactions and coarser responses to accumulated feedback. Vendors provide optimized backends that map signals and output controls to native NIC mechanisms with defined feedback and control semantics. We demonstrate PCM's expressivity by implementing eleven well-known datacenter CC and LB algorithms. We deploy PCM across four distinct datapaths: an Ultra Ethernet simulator, CPU-based UCCL, and on-NIC offloads on NVIDIA ConnectX-8 and Microsoft 1P. On CX-8, PCM-Swift achieves processing capacity equivalent to one reaction per three 4 KiB packets at 800 Gbit/s, using one-fifth of the NIC's hardware threads. In UCCL, it matches native Swift's 1.56 Tbit/s goodput with 64 KiB chunk-level control. PCM's design informed ongoing Ultra Ethernet work toward a portable congestion management interface.

cs.NI↗

arXiv2026

What Bloats Your Floats? Right-Sizing Numerical Precision for Scientific Computing

While FP64 (binary64) remains the standard representation of real numbers in scientific computing, hardware trends increasingly favor low-precision formats optimized for AI workloads. This shift prioritizes throughput and energy efficiency over full-precision capabilities. We challenge the necessity of high-precision arithmetic in PDE-governed simulations, where spatial and temporal discretization errors introduce a physical noise floor that masks the lower-order bits of the FP64 format, rendering them irrelevant. We propose a criterion based on the Signal-to-Noise Ratio (SNR) with respect to spatial and temporal refinements to evaluate the impact of safe precision reduction on scientific computations. Finally, we present an automated dataflow-centric workflow for targeted lowering within mixed-precision subgraphs in complex scientific applications. Large-scale evaluations of weather and climate applications show that strategic precision reduction on GPUs achieves up to 1.8x speedup without compromising physical fidelity.

cs.DC↗

arXiv2026

Causally Fair Generation with Large Language Models

Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape consequential decisions, raising concerns about their impact on demographic disparities. In this context, causal inference provides a principled basis for assessing fairness, because it attributes observed disparities to the mechanisms that generated them, which a purely statistical approach cannot do even with infinite data. In LLM generation, a query may request several causally related variables, each of which is both an outcome of interest and a possible cause of other outputs, and the information supplied in the prompt need not follow a topological or a temporal order. This calls for methods that can analyze and selectively remove disparities from such a flexible generation process. In this paper we introduce Causally Fair Generation with LLMs (CFG, for short). CFG extracts relevant concepts, grounds generation in a reference population and causal diagram, and removes user-selected causal effects. CFG also allows pathways deemed justifiable for the task's utility to be retained, which is known in legal literature as business necessity. Further, we provide formal guarantees for our method when eliminating all discriminatory causal effects in the adapted population model, under appropriate causal assumptions. We evaluate CFG with four LLMs in three real-world settings based on population data and on a synthetic dataset with a known causal ground truth.

cs.AI↗

arXiv2026

Which LLM to pick? Online Active Model Selection for Large Language Models

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 approximate real performance. While oracle annotations can provide reliable feedback, they are often costly and difficult to obtain at scale. To address this challenge, we propose ONLINE LLM PICKER, the first framework for active model selection for LLMs in online settings. Given an arbitrary stream of queries and a limited annotation budget, ONLINE LLM PICKER selects the most informative prompts for annotation to identify the best LLM among candidate models. Across multiple tasks including 10 datasets, for over 130 language models, we show that ONLINE LLM PICKER saves annotation cost by up to 71.67% while reliably identifying the best or near-best model for the stream. We also show that using the returned model for sequential generation on unannotated prompts across the stream reduces regret by up to a factor of 2.51x, indicating that ONLINE LLM PICKER can identify the best or near-best model well before processing all streaming prompts.

cs.CL↗

arXiv2026

FP64 Is All You Want, INT8 Is All You Need, FP4/6/8 Is All You Have

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 one family and pose the choice of a scheme as a combinatorial program that minimizes the number of low-precision GEMMs. Given, for each modulus, a finite set of ways to compute products modulo it from low-precision GEMMs, we find the choice of moduli and ways with the fewest GEMMs, for any format, accumulator and inner dimension, and derive lower bounds on the GEMM count over the whole family. Applied to the formats of current GPUs, the method gives the first FP6 schemes, an FP8 scheme with fewer GEMMs than any previous one, and an FP4 scheme that the bounds show needs the fewest GEMMs of any scheme in the family whose moduli lie in a stated range. Implemented on three Blackwell GPUs, the INT8, FP8 and FP4 schemes run faster than native FP64, up to 83x on B300.

cs.DC↗

arXiv2026

WUSH-KV: KV Cache Quantization with Data-Adaptive Transforms

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-bit KV-cache quantization. It adapts WUSH, which constructs a data-aware transform from the second-order statistics of both factors in a matrix product to reduce quantization error. WUSH-KV uses calibration data to construct separate key and value transforms, with the value transform folded into the model weights and the key transform applied after RoPE. The transforms can be paired with clipped quantizers. For one such quantizer, QuEST INT, we show that, under mild assumptions, the WUSH transform is near-optimal. With this quantizer, WUSH-KV reduces layerwise reconstruction error and achieves the lowest end-to-end perplexity among other tested transforms. For end-to-end evaluation, we integrate WUSH-KV into SGLang using OSCAR-style percentile-clipped affine quantization. At 2-bit, WUSH-KV performs comparably to or outperforms the OSCAR transform across all evaluated models and downstream tasks.

cs.LG↗

arXiv2026

Large Language Model Selection with Limited Annotations

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. To address this challenge, we develop SELECT-LLM, the first framework for active model selection of LLMs. SELECT-LLM aims to find a small set of queries whose annotations are most informative for identifying the best LLM for a given task. To this end, we introduce a query selection rule based on expected information gain, computed from pairwise similarities between candidate model outputs. Because this rule only uses generated model responses, SELECT-LLM can be applied across candidate models without assumptions about their architecture or access to model weights. This makes it suitable for both open-weight and black-box LLMs. We evaluate SELECT-LLM across 23 datasets, 156 evaluated models, diverse task families, and multiple text evaluation metrics. Across all experiments, SELECT-LLM improves over the strongest baseline in every setting, with annotation cost reductions up to 81.8% for best model selection and up to 84.78% for near-best model selection.

cs.CL↗

arXiv2026

Hardware-Aware Features for CUTLASS Kernel Selection

GPU libraries such as CUTLASS expose tens of thousands of semantically equivalent kernels for a single operation, making exhaustive autotuning expensive and execution-free selection difficult. Existing analytical selectors require hand-designed performance rules, while learned selectors operate on raw configuration parameters and must infer hardware consequences from data. We introduce a hardware-aware representation for CUTLASS kernel selection that augments candidate configurations with statically computable estimates of induced hardware behavior. We construct a dataset of 4.9 million CUTLASS kernels and train gradient-boosted and neural learning-to-rank models to rank candidates within each problem. On held-out exhaustive evaluation problems, hardware-aware representations reduce selection regret by up to 40\% relative to structural baselines and 64.2\% relative to NVIDIA's matrix-multiply heuristics. We further evaluate data-efficient cross-precision and epilogue-fusion transfer within CUTLASS GEMM, showing that explicitly representing candidate-induced hardware behavior provides a useful inductive bias for learned kernel selection.

cs.LG↗

arXiv2026

Large Language Model Selection with Limited Annotations

cs.CL↗

arXiv2026

Gauss What You Need: Compact Gaussian Splatting Across Scene Scales

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 scene affects reconstruction quality, storage, and rendering cost. How to select this number automatically across capture scales remains unresolved: configurations effective on standard benchmarks can leave larger captures with too few Gaussians to reconstruct fine details. We observe that the surface to represent, given by the capture's extent and resolution, is known before training, whereas its content complexity becomes apparent during training, through the reconstruction quality on the training views. We introduce TangoGS, which combines capture-derived model sizing with training-based adaptation: the capture determines the scale of the model, and training feedback determines its final size within that scale. Before training, TangoGS derives a learning allowance for model growth from the capture's total pixels after discounting views that re-observe the same scene points. During training, reconstruction quality guides how many Gaussians to add and remove. On 13 standard benchmark scenes, TangoGS matches the mean PSNR of the best-performing evaluated baseline, LeGS, with $48\%$ fewer Gaussians. On eight large captures, the same configuration automatically scales to larger models when necessary, achieving the highest mean PSNR among evaluated methods: $0.54$ dB above the runner-up with $2.3\times$ as many Gaussians. Together, capture-derived learning allowances and training-quality guided density control enable a state-of-the-art quality--size compromise across scene scales without retuning.

cs.CV↗

arXiv2026

The Canonical Parallel Form as a Substrate for Parallelizing Compilers and Agentic Optimizers

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 Canonical Parallel Form (CPF), a device-neutral program state from which every ordering constraint our analyses prove unnecessary has been removed. CPF is reached by output-preserving normalization, by lifting semantic operations such as tensor contractions, and by deriving parallelism in three levels ordered by decidability: syntactic subscript tests, exact affine dependence tests over integer sets, and SMT queries over non-linear integer arithmetic, which also admit parallelism guarded behind a runtime check. Heuristics can then specialize the canonical form for each architecture. Across 248 loop-level reasoning kernels on an AMD MI300A, CPF reaches 4.4x over Numba on its 24 Zen 4 cores and 25.4x on its CDNA 3 GPU. Against the other auto-parallelizing optimizers, CPF is 2.9x faster on the CPU and 8.7x on the GPU than DaCe's own auto-parallelizer, 1.9x faster than Pluto, and 1.5x faster than PPCG on the affine subset of the kernels. Because the pipeline is deterministic, CPF also serves as an agent's starting source, cutting the token cost per kernel by up to a factor of 2.72x while leaving the achieved speed-up unchanged, since the coding agents reason less about parallelism.

cs.PF↗

arXiv2026

Quantum computers will not be that different: A blueprint for quantum computer architecture at scale

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 accelerators. This paper argues that utility-scale quantum architecture is primarily a cost-performance problem across a coupled quantum-classical system, leading to a blueprint for scalable quantum processing unit (QPU) design. Our architecture blueprint is organized around clean logical abstractions, hiding details and complexity of physical qubit modalities below the instruction set architecture (ISA) boundary, and specializes recurring functions aggressively to minimize the cost for utility scale quantum computations. Its low-level implementation through specialized local hardware for control, readout, and quantum error correction (QEC) closely resembles the architecture of high-performance network stacks. One of our main insights is that the design principles and the resulting architecture closely follow established practice from classical computing and networking.

quant-ph↗

arXiv2026

Extreme-Scale Linear-Scaling Kohn-Sham DFT at 100 Million Atoms: Bridging Quantum Simulations and Experiments

Kohn-Sham density functional theory (DFT) remains the workhorse of ab initio materials simulation, yet cubic computational and quadratic memory scaling have confined calculations to a few hundred to thousands of atoms, spanning only nanometers, far below experimentally relevant length scales. We introduce XLSDFT, a linear-scaling DFT framework based on divide-and-conquer decomposition of the one-particle density matrix and Chebyshev-filtered subspace iteration, achieving linear computational and memory scaling while retaining DFT accuracy. Deployed on the LineShine exascale supercomputer, XLSDFT reduces computational complexity by orders of magnitude, enabling unprecedented DFT scale: a 200-million-atom silicon crystal, twentyfold beyond the prior record. Our implementation achieves 96.6% weak-scaling efficiency and sustained 157.9 Pflop/s (FP64) for a 100-million-atom scaling study. We further simulate an 11-million-atom all-solid-state battery interface of unprecedented complexity, 1,000 times beyond prior DFT for such systems, revealing how lithium metal reacts with the solid electrolyte at atomic resolution, in quantitative agreement with spectroscopy experiments.

cs.CE↗

arXiv2026

Performance Foundations of Parallel & Distributed Reasoning Language Models

Reinforcement Learning with Verifiable Rewards (RLVR) and other RL-style post-training paradigms have been used for aligning large language models (LLMs) with reasoning standards. The resulting recent Reasoning Language Models (RLMs) such as DeepSeek-R1, o3, and Kimi k1.5 show that such RL-style post-training ("RL-for-LLMs") can substantially improve chain-of-thought reasoning, long-horizon planning, and self-correction. However, the computational footprint of these systems is massive: state-of-the-art RLM training requires millions of GPU-hours and tightly coupled multi-model pipelines that stress modern hardware far beyond classical supervised LLM training. This makes RLM training as much a parallel and distributed systems problem as an algorithmic one. In this work, to facilitate developing RLMs that are simultaneously high-performance, scalable, and cost-effective, we first systematize the RL-for-LLM paradigm and provide a compute-centric analysis of prominent post-training algorithmic frameworks: Proximal Policy Optimization (PPO), Group Relative Policy Optimization (GRPO), as well as their variants. Second, we develop a taxonomy of intra- and inter-model parallelism strategies for RL-for-LLMs, covering both traditional techniques (data, tensor, pipeline, sequence, context, and expert parallelism) as well as novel forms of parallelism and optimization techniques for multi-model RLM training, for example disaggregated placement, stage fusion, hybrid parallelism, and asynchronous execution. We harness the work-depth model of parallel computing to make our taxonomy and its insights rigorous and portable. Finally, we analyze existing RLM frameworks and we distill practical guidelines and outline open research directions for building scalable, fast, and cost-effective RLMs.

cs.LG↗

arXiv2026

Maia 200: A Software Defined Dataflow System for Large-scale AI Acceleration

We introduce Maia 200, an advanced AI accelerator delivering high performance-10 145 Tflop/s FP4 and 5072 Tflop/s FP8 within a 750W TDP and 7 TB/s HBM bandwidth. Maia exemplifies a new class of Software Defined Locally Accessed Dataflow Architectures (SDLA), which explicitly program dataflow engines to orchestrate highly specialized memories and data movement engines. This approach shifts the focus from today's thread-centric to data-movement-centric architecture, improving efficiency and scalability. Our taxonomy of data management, inspired by Flynn's classification, highlights how SDLA addresses challenges in modern AI computing. Maia 200 achieves significant cost and energy savings while supporting massive parallelism for AI inference workloads, making it a compelling solution for next-generation high-performance computing systems.

cs.AR↗