Off-load Server Software Placement for GPU and FPGA Acceleration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In the context of cloud-first applications, using accelerators like GPUs and FPGAs for performance and cost-effective operation is challenging due to the need for code conversion, resource allocation, and optimal deployment locations, especially in IoT environments where image processing and other tasks require efficient processing without incurring significant delays or increased costs.
Innovation Solution
A software deployment method that analyzes application source code, identifies off-loadable processes, performs code conversion, and optimizes resource allocation and deployment locations based on performance and cost metrics, using techniques such as genetic algorithms to select the most efficient off-load patterns and resource configurations for GPUs and FPGAs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If code conversion and manual configuration for GPU/FPGA offloading is performed, then processing performance is improved, but system complexity and difficulty of operation increase
Solution Approach 1:
The system automatically analyzes application source code, identifies offloadable processes, performs code conversion, and selects optimal deployment configurations without requiring manual user intervention. The off-load server autonomously completes tasks that previously required skilled programming knowledge of CUDA/OpenCL and manual hardware configuration.
Solution Approach 2:
The off-load server acts as an intermediary between the application developer and the accelerator hardware (GPU/FPGA). It provides automated code conversion and optimization services, shielding users from the complexity of direct hardware programming while delivering performance benefits through intelligent code transformation and configuration selection.
2Speed
If more accelerator resources (GPU/FPGA) are allocated, then processing speed is improved, but deployment complexity and resource allocation difficulty increase
Solution Approach 1:
The system automatically adjusts deployment parameters including code conversion strategies, resource allocation configurations, and optimization settings based on the specific application requirements. It evaluates multiple parameter combinations and selects the optimal configuration for each application, eliminating the need for manual parameter tuning by users.
3Productivity
If manual code conversion and optimization is performed, then processing efficiency is improved, but development time and cost increase
Solution Approach 1:
The system performs code analysis, offloadable process identification, and optimization strategy selection in advance before actual deployment. By preparing optimized code and configuration upfront through automated analysis, it eliminates the need for time-consuming manual optimization processes while maintaining high processing efficiency.
Data Source
AI summary
A software deployment method includes: analyzing a source code of an application; designating off-loadable processes of the application; performing a code conversion of the application according to a deployment destination environment; measuring the performance of the converted application on a verification device; making a setting for resource amounts according to the deployment destination environment; selecting a deployment place by calculating a deployment destination on the basis of a performance and a cost when the converted application is deployed while ensuring the resource amounts; performing, after deployment to an actual environment, a performance measurement test process to measure an actual performance of application; and performing, after performing the performance measurement test process, one or more of performing the code conversion, making the setting for resource amounts, selecting the deployment place, measuring the performance of the application on the verification device, and performing the performance measurement test process.