Systems & Networks · Computer Science & AI
Performance
The newest papers in Performance that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
158 papers, from 4 September 2026 to 5 November 2026: 158 arXiv preprints.
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Newest papers, page 4
The Optimal Ranking Tradeoffs Between Two Ranking Scores in Two-Class Classification
The accuracy, true negative rate (negative class recall), true positive rate (positive class recall), negative predictive value (negative class precision), positive predictive value (positive class…Peak-Memory Schedules You Can Trust: Exact Realization, Certified Optimality, and the Limits of Recomputation for Inference
Real-time inference on small devices needs low activation memory without extra latency, which makes operator order the most valuable memory lever: it is free in time.Data-Driven Prediction of Optimal MPI Rank Placement and Node Count for Memory-Bound CFD on AMD EPYC Clusters
Users who run a memory-bound OpenFOAM case on an AMD EPYC cluster must decide how many nodes to use and how many MPI ranks to place on each core complex.Scaling LLM Inference via Parallelism: Throughput-Latency Trade-off
Large Language Model (LLM) serving systems must balance latency and throughput while efficiently utilizing distributed GPU resources.Delta Tokenizer: Reference-Identical Tokenization of Session Deltas and Segment-Parallelism for Long Prompts on Multicore CPUs
An agent that drives an LLM sends the model the same conversation again and again, and each turn adds only a little text at the end.AlignPred: Error-Controlled KV Cache Compression via Alignment and Predictive Coding
Long-running agents retain KV caches across turns and sessions for hours or days.How Reliable Are Large Language Models for Soil Organic Carbon Assessment? An Australian Benchmark
Soil organic carbon (SOC) is an indicator of agricultural soil condition and carbon storage, but numerical outputs from large language models (LLMs) require validation before use in monitoring or…Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources
We study online task assignment with reusable resources.Learning in Strategic Queuing with Geometric Recharge: From Bandits to Systems
We study packet routing in queuing systems where queues act as selfish, myopic agents that use no-regret learning algorithms to select servers with unknown service rates.GPU Goldmines: Tunable WebGPU Kernels for Edge LLM Inference
As demand for LLM inference grows and consumer GPUs grow more capable, edge deployment is becoming increasingly attractive.Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving
Large Language Model (LLM) inference is driving a growing share of datacenter carbon emissions.UAV-Mounted Hybrid RIS-Assisted ISAC-RSMA: Performance Analysis with Imperfect CSI and SIC
In integrated sensing and communication (ISAC) systems, strict sensing requirements can substantially constrain the transmit power available for communication.VLCQ: Post-training quantization for deep neural networks using variable length coding
Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices.Taking Tokenization off the Host for Agentic LLM Serving
Agentic workloads resend long contexts that barely change between calls.Measuring the Inference Energy Efficiency of Open-Weight LLMs across Benchmarks, Reasoning Modes, and Hardware
Benchmark scores tell us what an LLM can do, but not how much energy is required to deliver that capability.Read What Matters: Query-Adaptive Quantization for KV Caches
KV-cache entries are stored before their future queries are known, but each decoding query needs precision in different places.Energy-Aware Receiver-initiated MAC Protocol for Energy Harvesting IoT
Energy harvesting (EH) technology has emerged as a key approach for extending the operational autonomy of Internet of Things (IoT) devices.Reproducible LLM Inference Benchmarking: A Sequential Isolation Protocol for Regression Testing
Reproducible benchmarking of Large Language Model (LLM) inference is challenging because repeated measurements can vary with execution and system state.The Missing Fourth Term for the Emulation Tensor Memory Equilibrium (TME) Model: The Residue Deconstruction Cost
The Tensor-Memory Equilibrium (TME) model of "FP8 is All You Need (Part 1)" calculates the execution time of Ozaki Scheme II emulation of fp64 as the maximum of a tensor-core term and a…GPU Acceleration of Awkward Arrays: Using Python cuda.compute
Awkward Array is a widely used library in high-energy physics (HEP) for representing and manipulating nested, variable-length data in Python.