Accordion: A Simple and Effective Communication Scheduling Algorithm
If you are interested in machine learning, you might have heard about a communication scheduling algorithm called "Accordion." But what is Accordion, and how does it work?
Accordion is a gradient communication scheduling algorithm that is designed to work across different models without requiring additional parameter tuning. It is a simple yet effective algorithm that dynamically adjusts the communication schedule based on th
AutoSync is a powerful tool in the world of machine learning. It is a pipeline that optimizes synchronization strategies automatically, which is useful in data-parallel distributed machine learning.
What is AutoSync?
AutoSync is a system that optimizes synchronization strategies in machine learning. It uses factorization to organize the strategy space for each trainable building block of a deep learning (DL) model. With AutoSync, it is possible to efficiently navigate the strategy space and f
Understanding BAGUA
BAGUA is a communication framework used in machine learning that has been designed to support state-of-the-art system relaxation techniques of distributed training. Its main goal is to provide a flexible and modular system abstraction that is useful in the context of large-scale training settings.
Unlike traditional communication frameworks like parameter server and Allreduce paradigms, BAGUA offers a collection of MPI-style collective operations that can be used to facilit
Blink communication is a library that helps computers communicate with each other effectively. It is specially designed for inter-GPU parameter exchange and optimizes link utilization to deliver near-optimal performance. This library is ideal for clusters that have different hardware generations or partial allocations from cluster schedulers as it dynamically generates optimal communication primitives for a given topology.
Topology Heterogeneity Handling
Blink can handle topology heterogeneit
What is BytePS?
BytePS is a method used for training deep neural networks. It is a distributed approach that can be used with varying numbers of CPU machines. BytePS can handle traditional all-reduce and parameter server (PS) as two special cases within its framework.
How does BytePS work?
BytePS makes use of a Summation Service and splits a DNN optimizer into two parts: gradient summation and parameter update. For faster DNN training, the CPU-friendly part, gradient summation, is kept on CP
Distributed deep neural network training can be a complex process, especially when it comes to communication between nodes. This is where ByteScheduler comes in. ByteScheduler is a communication scheduler designed specifically to optimize distributed DNN training acceleration.
What is ByteScheduler?
ByteScheduler is a generic communication scheduler for distributed deep neural network (DNN) training. It is based on the idea that rearranging and partitioning tensor transmissions can lead to op
Understanding Chimera: A Pipeline Model Parallelism Scheme
Chimera is a model parallelism scheme designed to train large-scale models efficiently. Its unique feature is the combination of bidirectional pipelines, namely down and up pipelines, to accomplish the task. The aim is to execute a large number of micro-batches by each worker within a training iteration with the minimum of four pipeline stages.
How Chimera Pipeline Works?
Chimera pipeline, as shown in the figure, consists of four pip
Overview of DistDGL: A System for Training Graph Neural Networks on a Cluster of Machines
DistDGL is a system that enables the training of Graph Neural Networks (GNNs) using a mini-batch approach on a cluster of machines. This system is based on the popular GNN development framework, Deep Graph Library (DGL). With DistDGL, the graph and its associated data are distributed across multiple machines to enable a computational decomposition method, following an owner-compute rule.
This method allow
DABMD: An Overview of Distributed Any-Batch Mirror Descent
If you've ever waited for slow internet to load a webpage, you know the feeling of frustration that comes with waiting for information to be transferred between nodes on a network. In distributed online optimization, this waiting can be particularly problematic. That's where Distributed Any-Batch Mirror Descent (DABMD) comes in.
DABMD is a method of distributed online optimization that uses a fixed per-round computing time to limit the
Overview of Dorylus: A Distributed System for Training Graph Neural Networks
Dorylus is a distributed system used for training graph neural networks. This system is designed to use affordable CPU servers and Lambda threads to scale up to billion-edge graphs while utilizing low-cost cloud resources.
Understanding Graph Neural Networks
Graph neural networks (GNNs) are a type of machine learning algorithm that uses graph structures to solve complex problems. These graphs consist of nodes and ed
FastMoE is a powerful distributed training system built on PyTorch that accelerates the training process of massive models with commonly used accelerators. This system is designed to provide a hierarchical interface to ensure the flexibility of model designs and the adaptability of different applications, such as Transformer-XL and Megatron-LM.
What is FastMoE?
FastMoE stands for Fast Mixture of Experts, a training system that distributes training for models across multiple nodes. Its primary
Are you familiar with deep learning engines? FlexFlow is one of them which uses guided randomized search of the SOAP space to find a fast parallelization strategy for a specific parallel machine. Let's find out more about it!
What is FlexFlow?
FlexFlow is a powerful deep learning engine that is designed to optimize parallelization strategy for a specific parallel machine. It utilizes a guided randomized search of the SOAP space to accomplish this task. FlexFlow introduces a novel execution si
GPipe is a distributed model parallel method for neural networks that allows for faster and more efficient training of deep learning models.
What is GPipe?
GPipe is a distributed model parallel method for neural networks that was developed by Google to improve the efficiency and speed of training deep learning models. It works by dividing the layers of a model into cells, which can then be distributed across multiple accelerators. By doing this, GPipe allows for batch splitting, which divides
Overview of Gradient Sparsification
Gradient Sparsification is a technique used in distributed machine learning to reduce the communication cost between multiple machines during training. This technique involves sparsifying stochastic gradients, which are used to calculate the weights of the machine learning model. By reducing the number of coordinates in the stochastic gradient, Gradient Sparsification can significantly decrease the amount of data that needs to be communicated between machines
Have you ever been frustrated by slow or inefficient neural network computations? If so, you may be interested in GShard, a new method for improving the performance of deep learning models.
What is GShard?
GShard is an intra-layer parallel distributed method developed by researchers at Google. Simply put, it allows for the parallelization of computations within a single layer of a neural network. This can drastically improve the speed and efficiency of model training and inference.
One of th
What is Herring?
Herring is a distributed training method that utilizes a parameter server. It combines Amazon Web Services' Elastic Fabric Adapter (EFA) with a unique parameter sharding technique that makes better use of the available network bandwidth. Herring utilizes a balanced fusion buffer and EFA to optimally utilize the total bandwidth available across all nodes in the cluster while reducing gradients hierarchically, reducing them inside the node first, and then across nodes.
How Does
Introduction to HetPipe
HetPipe is a revolutionary parallel method that combines two different approaches, pipelined model parallelism and data parallelism, for improved performance. This innovative solution allows multiple virtual workers, each with multiple GPUs, to process minibatches in a pipelined manner, while simultaneously leveraging data parallelism for superior performance. This article will dive deeper into the concept of HetPipe, its underlying principles, and how it could change th
What is IMPALA?
IMPALA, which stands for Importance Weighted Actor Learner Architecture, is an off-policy actor-critic framework. The framework separates acting from learning and allows learning from experience trajectories using V-trace. IMPALA is different from other agents like A3C because it communicates trajectories of experience to a centralized learner rather than gradients with respect to the parameters of the policy to a central parameter server. The decoupled architecture of IMPALA al