Unified Multi-Node Launcher for HPC and Deep Learning Workloads

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Solution Overview

Problem

Current multi-node launchers in the HPC domain are not aware of deep learning framework setup requirements and are not integrated with DL workloads, making them ineffective when used inside containers, and lack a unified launch and termination mechanism for both HPC and DL applications.

Innovation Solution

A multi-node launcher utility that provides a unified launch and termination mechanism for HPC and DL applications using a single API, capable of handling frameworks like MPI, PyTorch, and Tensorflow, and operates within containers without requiring platform-specific setups.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If current multi-node launchers are used in HPC domain, then they can execute computational operations, but they are not aware of deep learning framework setup requirements and cannot operate within containers

Engineering Contradiction:

Improvecompatibility with deep learning frameworks and containersVSAvoidexecution reliability in container environments

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The launcher is designed to provide a unified interface that can execute both HPC applications and deep learning workloads across multiple nodes. It supports multiple deep learning frameworks (PyTorch, TensorFlow, etc.) and can operate in both containerized and non-containerized environments through a single standardized API, eliminating the need for framework-specific or environment-specific launchers.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Productivity

If computational operations are distributed to various systems, then resource utilization is improved, but execution delay increases due to coordination overhead

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidexecution delay

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The launcher performs preliminary actions by pre-configuring the execution environment on all target nodes before the actual computational workload begins. This includes setting up framework-specific environments, allocating resources, and establishing communication channels in advance, so that when the workload is distributed, the nodes are already ready to execute without coordination delays.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If multiple launchers are used for different frameworks, then each framework can be optimized, but system complexity increases

Engineering Contradiction:

Improveframework-specific optimizationVSAvoidnumber of launcher utilities

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The invention merges the functionality of multiple framework-specific launchers into a single unified launcher. This unified launcher internally handles framework-specific configurations and optimizations while presenting a consistent interface to users, thereby reducing system complexity from multiple separate utilities to one consolidated tool that manages all deep learning frameworks and HPC applications.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20240070048A1Application programming interface to monitor software workloads

Publication Date: 2024.02.29 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to perform software workloads. In at least one embodiment, one or more circuits of a processor perform a first application programming interface to select a second application programming interface, wherein the second application programming interface monitors performance of one or more software workloads identified by the first application programming interface.