Parallel Computing Frameworks

Training large-scale models—particularly LLMs with hundreds of billions or even trillions of parameters—poses unique system-level challenges. Memory limits, communication bottlenecks, and uneven compute loads can quickly bring naïve training strategies to a halt. Relying on just one form of parallelism (e.g., data parallelism alone) simply doesn’t scale effectively. Instead, modern deep learning frameworks and teams combine multiple forms of parallelism to stretch hardware capabilities to their limits. Each strategy addresses a different bottleneck: ➜ Data parallelism boosts throughput by replicating the model across nodes. ➜ Tensor/model parallelism breaks up massive weight matrices. ➜ Pipeline parallelism improves utilization across deep architectures. ➜ Expert parallelism adds sparsity and dynamic routing for efficiency. ➜ ZeRO optimizes memory allocation down to optimizer states and gradients. ➜ Context parallelism (a newer strategy) allows for splitting long sequences—critical for LLMs handling multi-thousand-token contexts. This modular, composable approach is the backbone of training breakthroughs seen in models like GPT-4, PaLM, and beyond. Link to the article: https://lnkd.in/gZBF-N2w

 📖 "The Ultra-Scale Playbook" by 🤗 Hugging Face Team 👏 👏 👏 >> The Challenge: How do you train massive LLMs (7B-405B parameters) efficiently across 100s of GPUs when a single model doesn't even fit in memory? >> The Research: 4,100+ distributed experiments on up to 512 H100 GPUs, testing every possible configuration to find what actually works. >> Key Insights: * 5D Parallelism Framework: Data, Tensor, Pipeline, Sequence, and Expert parallelism - each solving different bottlenecks. * Memory > Compute: Your first bottleneck is always memory (70B model needs 1.4TB!), not FLOPS. (That very very true! As things do not necessary get broken being slow but having lack of memory). * 10x Speed Difference: Intra-node (NVLink) vs inter-node communication drives architecture decisions. * ZeRO-2 Sweet Spot: Best memory/communication tradeoff for most scenarios. * Flash Attention Revolution: 72% memory reduction, 1.5x speedup - now standard in all transformers. >> Practical Takeaway: No silver bullet exists. Optimal configuration depends on model size, cluster topology, and batch size targets. The playbook provides the decision tree: * <10B models: Simple Tensor Parallelism. * 10-100B: TP + Pipeline Parallelism. *100B+: Full 5D parallelism required. >> Why This Matters: Previously, this knowledge was locked within OpenAI, Google, and Meta. Now it's open source with working code (Nanotron framework). The democratization of large-scale AI training knowledge continues. #AI #MachineLearning #DistributedComputing #LLM #Engineering #HuggingFace Ref: https://lnkd.in/gmtv5vrP

I haven’t released many new tools in the past few months, so get ready… there’s an avalanche coming 😅 First up is ForkUnion v3: arguably the fastest and most hardware-friendly parallelism library on GitHub. Across a fleet of really fat 100+ core NUMA machines on Nebius, it delivers 3–6× lower latency than OpenMP, and 12–16× lower latency than Taskflow in C++ and Rayon in Rust. Most “thread pools” today are still std::deque<std::shared_ptr<task_t>> guarded by a std::mutex. That works well enough until you start scaling RL workloads or simulation environments across 100+ cores, where heap allocations, mutexes, and Compare-And-Swap atomics become the bottleneck. ForkUnion is designed around hardware instead of language abstractions. No dynamic memory allocations, no mutexes, no CAS atomics. It understands NUMA topology, measures compute-to-memory affinity itself, and leverages modern ISA features like WFET on Arm, Zawrs on RISC-V, and cache-line demotion on x86. It supports C, C++, Rust, and Zig, and my hope is that one day it becomes the “LibC of parallelism” for systems software. More releases are coming soon 🤗 https://lnkd.in/eYj63B34

As an AI Engineer, you must understand how these NVIDIA frameworks/libraries work 👌 1️⃣ 𝗖𝗨𝗗𝗔 A parallel computing platform and API to accelerate computation on NVIDIA GPUs. Keypoints: ↳ Kernels - C/C++ functions. ↳ Thread - executes the kernel instructions. ↳ Block - groups of threads. ↳ Grid - a collection of blocks. ↳ Streaming Multiprocessor (SM) - processor units that execute thread blocks. When a CUDA program invokes a kernel grid, the thread blocks are distributed to the SMs. CUDA follows the SIMT (Single Instruction Multiple Threads) architecture to execute thread logic and uses a Barrier to gather and synchronize Threads. 2️⃣ 𝗰𝘂𝗗𝗡𝗡 Library with highly tuned implementations for standard routines such as: ↳ forward and backward convolution ↳ attention ↳ Matmul, pooling, and normalization are used in all neural network architectures. 3️⃣ 𝗧𝗲𝗻𝘀𝗼𝗿𝗥𝗧 If we unpack a model architecture, we have multiple layer types, operations, layer connections, activations, etc. Imagine an NN architecture as a complex Graph of operations. TensorRT can: ↳ Scan that graph ↳ Identify bottlenecks ↳ Optimize ↳ Remove, merge layers ↳ Reduce layer precision, ↳ Many other optimizations. 4️⃣ 𝗧𝗲𝗻𝘀𝗼𝗿𝗥𝗧-𝗟𝗟𝗠 Inference Engine that leverages TensorRT Compiler optimizations for Transformer-based models. Covers the advanced and custom requirements for LLMs, such as: ↳ KV Caching ↳ Inflight Batching ↳ Optimized Attention Kernels ↳Tensor Parallel ↳ Pipeline Parallel. 5️⃣ 𝗧𝗿𝗶𝘁𝗼𝗻 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗦𝗲𝗿𝘃𝗲𝗿 An open-source, high-performance, and secure serving system for AI Workloads. Devs can optimize their models, define serving configurations in Protobuf Text files, and deploy. It supports multiple framework backends, including: ↳ Native PyTorch, TensorFlow ↳ TensorRT, TensorRT-LLM ↳ Custom BLS (Business Language Scripting) with Python Backends 6️⃣ 𝗡𝗩𝗜𝗗𝗜𝗔 𝗡𝗜𝗠 Set of plug-and-play inference microservices that package up multiple NVIDIA libraries and frameworks highly tuned for serving LLMs to production clusters & datacenters at scale. Contains: ↳ CUDA, cuDNN ↳ TensorRT ↳ Triton Server ↳ Many other libraries are baked in. NIM provides the optimal serving configuration for an LLM. 7️⃣ 𝗗𝘆𝗻𝗮𝗺𝗼 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 The Triton Inference Server successor, but for large-scale GenAI workloads. Composed of modular blocks, robust and scalable. Implements: ↳ Elastic Compute - GPU Planner ↳ KV Routing, Sharing, and Caching ↳ Disaggregated Serving of Prefill and Decode. 🗒️ For a deeper dive, code examples and diagrams, see the first comment! --- #deeplearning #artificialintelligence #machinelearning #LLM --- ♻️ Share this with your network. 💡 Follow me for practical content on AI/ML!

Don’t settle for a toy model. Distributed training is the key to scaling a prototype model to an enterprise model. But distributed systems have a lingo of their own. So here’s an intro. Distributed learning is the practice of training a single model using multiple GPUs or machines, which are coordinated to work in parallel by distributing the data, the model, or both. GPUs are processors with cores that are optimized for parallel computing, which is exactly what we want in distributed training. We want model training to happen in parallel. Parallelization strategies are ways to splits the task of training a model across different resources. 📊 Data Parallelism: Replicates the model, split the data. ✨ Model Parallelism: Splits the model's layers across GPUs. 🔩 Pipeline Parallelism: Splits the model, process it like an assembly line. 🧊 Tensor Parallelism: Splits a single layer's tensors across GPUs. But distributed training isn’t only about GPUs. Sometimes your model’s footprint may be too big for a single server (also called a node) or you may need more GPUs than a single server can hold. In this case, you would scale to multi-node training. The easiest way to scale your training job is to use cloud compute. These companies generally fall into a few categories (with some overlap): • Traditional Public Cloud: Wide array of services, including GPUs, as a small part of their overall infrastructure (e.g. Amazon Web Services (AWS), Microsoft Azure, Google Cloud). • Specialized GPU Cloud Providers: Focus exclusively on providing purpose-built GPU hardware and infrastructure for AI and machine learning workloads (e.g. Runpod, Lambda, Nebius, CoreWeave). • Serverless GPU Platforms: Platforms that abstract away infrastructure management, allowing users to deploy and scale models on-demand with a simple API call (e.g. Modal, Baseten). • Decentralized Compute: A network that pools computing power from a distributed network of individually owned machines to provide a collective resource (e.g. Prime Intellect). When you want to implement distributed learning in Python, you have several options. These frameworks fall into low- and high-level categories. Low-level frameworks like Ray (Anyscale), PyTorch, DeepSpeed.ai, and Accelerate (Hugging Face) serve as the building blocks of distributed learning, giving you maximum control, flexibility, and ability to customize your training pipelines. High-level frameworks like Axolotl and Unsloth AI specialize specifically in model fine-tuning, abstracting away the complexity of the lower-level frameworks. They make it easy to get started by providing ready-to-use solutions for specific fine-tuning tasks. There’s a lot more to scaling your model training than just this. If you’re interested in learning more, check out Zachary Mueller's course Scratch to Scale, which starts this September. 🔗 Scratch to Scale: https://lnkd.in/gKKuzaaH

We released Alto, a new system for orchestrating distributed compound AI applications that automatically streams and parallelizes execution across components like language models, retrievers, and rerankers. ℹ️ Compound AI applications chain together subcomponents such as generative language models, document retrievers, and embedding models. Applying traditional systems optimizations such as parallelism and pipelining in compound AI systems is difficult because each component has different constraints in terms of the granularity and type of data that it ingests. New data is often generated during intermediate computations, and text streams may be split into smaller, independent fragments (such as documents to sentences) which may then be re-aggregated at later parts of the computation. Due to this complexity, existing systems to serve compound AI queries do not fully take advantage of parallelism and pipelining opportunities. 💡 We present Alto, a framework that automatically optimizes execution of compound AI queries through streaming and parallelism. Bento introduces a new abstraction called nested ancestry, a metadata hierarchy that allows the system to correctly track partial outputs and aggregate data across the heterogeneous constraints of the components of compound AI applications. This metadata is automatically inferred from the programming model, allowing developers to express complex dataflow patterns without needing to reason manually about the details of routing and aggregation. 📈  Implementations of four applications in Alto outperform or match implementations in LangGraph, a popular existing AI programming framework. Alto implementations match or improve latency by between 10-30%. Link to the paper: https://lnkd.in/dV5D9b25

𝗠𝗼𝗱𝗲𝗹 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺 - how modern AI actually scales beyond one GPU When you hear about training 70B+ parameter models, the real magic isn’t in the model design - it’s in how the model is distributed across GPUs. Here’s the simple breakdown  • 𝗗𝗮𝘁𝗮 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 (𝗗𝗣) → Each GPU trains on a different slice of data, but holds a full copy of the model. Easy to use, but memory-heavy.  • 𝗭𝗲𝗥𝗢 (𝗦𝗵𝗮𝗿𝗱𝗲𝗱 𝗗𝗗𝗣) → Smarter DP. Each GPU holds only part of the weights and optimizer states. They sync on demand, saving massive memory.  • 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 (𝗣𝗣) → Split the model by layers — like a conveyor belt. Each GPU handles a stage and passes activations to the next. Great for huge models.  • 𝗧𝗲𝗻𝘀𝗼𝗿 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 (𝗧𝗣) → Split the math itself. Each GPU works on a slice of the same tensor, then syncs the results. Used in Megatron-LM and DeepSpeed for ultra-large transformers. Combine them, and you get: 1️⃣ DP + PP → 2D parallelism for efficiency 2️⃣ DP + PP + TP → 3D parallelism for trillion-scale models 3️⃣ Add ZeRO, and you can train models that don’t even fit in GPU memory Frameworks like DeepSpeed, Megatron-LM, Varuna, and SageMaker have made this orchestration almost plug-and-play. Bottom line: - Model Parallelism isn’t just a scaling trick, it’s how foundation models exist. - Understanding it isn’t optional anymore, it’s what separates training demos from real distributed AI systems. ----------------------- Find me → Aakriti Aggarwal ✔️ I build & teach stuff around LLMs, AI Agents, RAGs & Machine Learning! #AIInfra #SystemDesign #MLOps #DistributedAI #LLMTraining