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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
ABET Accreditation: A Way Forward for PDC Education
Sherif G. Aly, Haidar Harmanani, Rajendra K. Raj, Sanaa Sharafed · 2021-05-05 · via cs.DC updates on arXiv.org

With parallel and distributed computing (PDC) now wide-spread, modern computing programs must incorporate PDC within the curriculum. ACM and IEEE Computer Society's Computer Science curricular guidelines have recommended exposure to PDC concepts since 2013. More recently, a variety of initiatives have made PDC curricular content, lectures, and labs freely available for undergraduate computer science programs. Despite these efforts, progress in ensuring computer science students graduate with sufficient PDC exposure has been uneven. This paper discusses the impact of ABET's revised criteria that have required exposure to PDC to achieve accreditation for computer science programs since 2018. The authors reviewed 20 top ABET-accredited computer science programs and analyzed how they covered the required PDC components in their curricula. Using their own institutions as case studies, the authors examine in detail how three different ABET-accredited computer science programs covered PDC using different approaches, yet meeting the PDC requirements of these ABET criteria. The paper also shows how ACM/IEEE Computer Society curricular guidelines for computer engineering and software engineering programs, along with ABET accreditation criteria, can cover PDC.