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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
Design and Implementation of Parallel Debugger and Profil...
Aleem Akhtar, Aamir Shafi, Mohsan Jameel · 2014-08-27 · via cs.DC updates on arXiv.org

MPJ Express is a messaging system that allows computational scientists to write and execute parallel Java applications on High Performance Computing (HPC) hardware. Despite its successful adoption in the Java HPC community, the MPJ Express software currently does not provide any support for debugging and profiling parallel applications and hence forces its users to rely on manual and tedious debugging/profiling methods. Support for such tools is essential to help application developers increase their overall productivity. To address this we have developed debugging and profiling tools for MPJ Express, which are the main topic of this paper. Key design goals for these tools include: 1) maintain compatibility with existing logging, debugging, and visualizing tools, 2) build these tools by extending existing debugging/profiling tools instead of reinventing the wheel. The first tool, named MPJDebug, builds on the open-source Eclipse Integrated Development Environment (IDE). It provides an Eclipse-based plugin developed using the Eclipse Plugin Development Environment (PDE). The default Eclipse debugger currently does not support debugging parallel applications running on a compute cluster. The second tool, named MPJProf, is a utility based on Tuning and Analysis Utility (TAU)-an open-source performance evaluation tool. Our goal here is to exploit TAU to profile Java applications parallelized using MPJ Express by generating profiles and traces, which can later be visualized using existing tools like paraprof and Jumpshot. Towards the end of the paper, we quantify the overhead of using MPJProf, which we found to be negligible in the profiling stage of parallel application development.