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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
TransForm: Formally Specifying Transistency Models and Sy...
Naorin Hossain, Caroline Trippel, Margaret Martonosi · 2020-08-09 · via cs.DC updates on arXiv.org

Memory consistency models (MCMs) specify the legal ordering and visibility of shared memory accesses in a parallel program. Traditionally, instruction set architecture (ISA) MCMs assume that relevant program-visible memory ordering behaviors only result from shared memory interactions that take place between user-level program instructions. This assumption fails to account for virtual memory (VM) implementations that may result in additional shared memory interactions between user-level program instructions and both 1) system-level operations (e.g., address remappings and translation lookaside buffer invalidations initiated by system calls) and 2) hardware-level operations (e.g., hardware page table walks and dirty bit updates) during a user-level program's execution. These additional shared memory interactions can impact the observable memory ordering behaviors of user-level programs. Thus, memory transistency models (MTMs) have been coined as a superset of MCMs to additionally articulate VM-aware consistency rules. However, no prior work has enabled formal MTM specifications, nor methods to support their automated analysis. To fill the above gap, this paper presents the TransForm framework. First, TransForm features an axiomatic vocabulary for formally specifying MTMs. Second, TransForm includes a synthesis engine to support the automated generation of litmus tests enhanced with MTM features (i.e., enhanced litmus tests, or ELTs) when supplied with a TransForm MTM specification. As a case study, we formally define an estimated MTM for Intel x86 processors, called x86t_elt, that is based on observations made by an ELT-based evaluation of an Intel x86 MTM implementation from prior work and available public documentation. Given x86t_elt and a synthesis bound as input, TransForm's synthesis engine successfully produces a set of ELTs including relevant ELTs from prior work.