performance optimization
5 notes, newest first.
Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
The authors present a method to split large machine learning models into smaller parts called pipeline shards, which can then be run on multiple Integrated AI Processors (AIPCs) over an ordinary network. They show that…
ArchAgent v2: A Case Study with the Data Prefetching Championship
The paper presents ArchAgent v2, a framework that automates the discovery of multi-level data prefetching in computer microarchitecture. It outperforms hand-designed solutions from the 4th Data Prefetching Championship…
Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
This research study explores the effectiveness of inference-time scaling for computer-use agents (CUAs) that run locally on devices. The authors investigate various dimensions such as context, time, structure, and…
HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference
The paper proposes HCRMap, a framework for managing hot expert replicas in 3.5D Mixture-of-Experts (MoE) inference systems. The aim is to dynamically place and reuse hot experts across different memory tiers to…
Are Performance-Optimization Benchmarks Reliably Measuring Coding Agents?
The paper evaluates three performance-optimization benchmarks (GSO, SWE-Perf, and SWE-efficiency) used to assess coding agents. The authors find that these benchmarks have issues with stability, scoring rules, and the…
