Resource Manager Node for HPC Cluster Topology-Aware Allocation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Conventional resource management systems for HPC cluster environments fail to efficiently manage heterogeneous resources, leading to suboptimal resource allocation and utilization, particularly when utilizing performance acceleration devices like GPUs, MICs, and FPGAs, and do not adequately consider network topology and communication costs, resulting in low computation performance efficiency.

Innovation Solution

A resource manager node with integrated modules for resource policy management, shared resource capability management, monitoring, and allocation, which determines optimal resource allocation policies based on task characteristics and node topology to allocate resources efficiently across heterogeneous nodes, optimizing performance and resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional resource management systems are used in HPC cluster environments, then resource allocation can be performed, but resource utilization and computation performance are suboptimal due to failure to consider heterogeneous resource characteristics

Engineering Contradiction:

Improvecomputation performance efficiencyVSAvoidheterogeneous resource management capability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent applies local quality by creating specific resource allocation policies tailored to different heterogeneous resource types (CPU, GPU, MIC, FPGA, memory). Each resource type receives customized management approaches based on its characteristics, enabling optimal utilization of diverse hardware components while maintaining overall system productivity

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically adjusts allocation parameters based on task characteristics and resource availability. The resource allocation policy manager modifies allocation decisions by considering multiple parameters including resource type, task requirements, current load, and performance metrics, thereby improving computation efficiency across heterogeneous resources

Inventive Principle:

Principle #35Parameter changes

2Productivity

If resources are allocated without considering network topology and communication costs, then allocation simplicity is maintained, but communication efficiency and overall system performance deteriorate

Engineering Contradiction:

Improvecommunication efficiencyVSAvoidallocation system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements preliminary action by pre-analyzing network topology and communication costs before resource allocation. The system prepares allocation strategies that anticipate communication patterns, allowing tasks to be assigned to nodes that minimize future communication overhead, thereby improving communication efficiency without adding runtime complexity

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The resource allocation policy manager acts as an intermediary that mediates between task requirements and resource availability. It incorporates network topology information and communication cost analysis into allocation decisions, balancing the need for communication efficiency with the desire to maintain manageable system complexity through centralized policy management

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If heterogeneous resources are not properly matched to task characteristics, then resource allocation is simplified, but resource utilization efficiency decreases

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidallocation management ease

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system applies local quality by matching specific resource types to task characteristics through customized allocation policies. Different task types (compute-intensive, memory-intensive, I/O-intensive) are allocated to appropriate heterogeneous resources (CPU, GPU, high-capacity memory nodes) based on their specific requirements, maximizing resource utilization efficiency

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The resource allocation system enables self-service by allowing task characteristics to automatically guide allocation decisions. The policy manager evaluates task requirements and autonomously selects appropriate resources without manual intervention, improving both utilization efficiency and operational ease through automated matching

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS8949847B2Apparatus and method for managing resources in cluster computing environment

Publication Date: 2015.02.03 ELECTRONICS & TELECOMM RES INST

AI summary

Disclosed herein are a resource manager node and a resource management method. The resource manager node includes a resource management unit, a resource policy management unit, a shared resource capability management unit, a shared resource status monitoring unit, and a shared resource allocation unit. The resource management unit performs an operation necessary for resource allocation when a resource allocation request is received. The resource policy management unit determines a resource allocation policy based on the characteristic of the task, and generates resource allocation information. The shared resource capability management unit manages the topology of nodes, information about the capabilities of resources, and resource association information. The shared resource status monitoring unit monitors and manages information about the status of each node and the use of allocated resources. The shared resource allocation unit sends a resource allocation request to at least one of the plurality of nodes.