Hardware Offloading Simulator Using Precomputed Output Data
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Solution Overview
Problem
Designing and optimizing hardware offloading systems for big data analytics is a time-consuming and costly process, and existing simulators struggle to accurately estimate performance due to inefficiencies when executed on CPUs, which cannot match the speed of higher performance processors like FPGAs or GPUs.
Innovation Solution
A simulator that receives input data, prepares output data without computing it, and returns the data after a simulated idle time, allowing it to perform more like the actual hardware offloading system by storing output data from previous runs and using a throughput metric to simulate the idle time, thus avoiding CPU computation inefficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a simulator executes computations using CPU threads to simulate hardware offloading, then the simulator can perform calculations, but the simulation speed and accuracy are limited by CPU performance compared to actual hardware accelerators
Solution Approach 1:
The system performs preliminary computations using the actual hardware accelerator (FPGA/GPU) during system setup or calibration phase. The results of these preliminary computations are stored and reused during subsequent simulations, eliminating the need for real-time CPU-based computation and achieving both high speed and accurate performance estimation
Solution Approach 2:
Instead of copying the hardware accelerator's computation logic to run on CPU, the system copies the pre-computed output data from hardware simulations to use during software simulations. This allows the simulator to achieve hardware-level accuracy without actually executing the computationally intensive operations on the simulator itself
2Productivity
If the simulator computes output data using CPU threads, then the simulator can produce results, but the computation time increases and does not reflect actual hardware performance
Solution Approach 1:
The system performs all necessary computations in advance using the actual hardware accelerator during an initialization or calibration phase. Once the pre-computed results are obtained, they can be rapidly retrieved and used during subsequent simulation runs, dramatically reducing simulation execution time while maintaining hardware-accurate performance characteristics
Solution Approach 2:
The system introduces pre-computed result data as an intermediary between the simulation input and the simulation output. Instead of the CPU directly computing results, the pre-computed hardware results serve as an intermediary that bridges the gap between simulation inputs and expected outputs, enabling fast and accurate simulation
Data Source
AI summary
Described are examples for simulating performance of a hardware offloading system including receiving, by a simulator that corresponds to a simulated architecture representing the hardware offloading system, input data from a user application for processing by the simulated architecture, preparing, by the simulator, corresponding output data for the input data without computing the corresponding output data by the simulated architecture, and returning, by the simulator, the corresponding output data to the user application after a simulated idle time related to computing the corresponding output data by the simulated architecture.