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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
ACEAPEX: Parallel LZ77 Decoding via Encode-Time Absolute ...
Yakiv Shavidze · 2026-06-03 · via cs.DC updates on arXiv.org

LZ77-based codecs exhibit a fundamental sequential bottleneck in decoding: each back-reference depends on previously decompressed data, preventing multi-core scaling. We present ACEAPEX, a parallel LZ77 codec that stores all back-references as absolute positions in the decompressed output and organizes data into self-contained 1 MB blocks, enabling embarrassingly parallel block-level decoding. Integrated into lzbench, ACEAPEX achieves 10,160 MB/s on EPYC 4344P (8 cores) and 10,869 MB/s on EPYC 9575F for FASTQ genomic data -- up to 3.1x faster than zstd -3 at comparable compression ratios. We further implement a GPU wavefront decoder on NVIDIA H100 SXM, measuring 44.0 GB/s on enwik9 and 20.3 GB/s on FASTQ (wavefront match phase, BIT-PERFECT verified). With a depth-limited encoder variant (-1.5% ratio on enwik9), GPU throughput reaches 77.2 GB/s on a single H100 and 249.9 GB/s on two H100s in NVLink configuration. To our knowledge, this is the first reported GPU LZ77 decode with near-standard compression ratio verified byte-for-byte.