Synchronization is a key step in data-parallel distributed machine learning (ML). Different synchronization systems and strategies perform differently, and to achieve optimal parallel training throughput requires synchronization strategies that adapt …

Feb 23, 2024 · In order to solve these problems, this paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning, and we call this method the Prediction Synchronous Parallel (PSP) method in this paper.

Use #1: Using parallelism to make linear algebra fast. We can get a major boost in performance by building linear algebra kernels (e.g. matrix multiplies, vector adds, et cetera) that use parallelism to run fast.

enables ma-chine learning computations to establish synchronization bar-riers during runtime. Sync-on-the-fly exploits the fact that synchronization in machine learning algorit.

The first part of this thesis presents a simple design principle, adaptive parallelism, that applies suitable parallelization techniques to model building blocks (e.g., layers) according to their specific ML properties.

This research paper delves into the exploration and evaluation of advanced parallel computing methodologies tailored for accelerating ML algorithms when applied to vast datasets. We commence by providing a comprehensive overview of the existing parallelization paradigms, highlighting their strengths and limitations in the context of ML.

In this paper, we focus on accelerating the parameter syn-chronization for peer-to-peer distributed learning, following the works of Orpheus [3], Malt [11], and SFB [10], and propose SELMCAST an expressive and Pareto-optimal mul-ticast receiver selection algorithm to achieve the goal.

Many machine learning algorithms are easy to parallelize in theory. However, the xed cost of creating a distributed system that organizes and manages the work is an obstacle to parallelizing existing algorithms and prototyping new ones. We present Qjam, a Python library that transpar-ently parallelizes machine learning algorithms that adhere

Taking many aspects which affect throughput of data-parallel training system into account, this paper use machine learning method to find optimal sync strategies, which achieves better result compared with hand-craft ones and the strategies also shows good transferability.

Exploring techniques to scale machine learning algorithms on distributed and high performance systems can potentially help us tackle this problem and increase the pace of development as well as the accessibility of machine learning research.