Solver-Hardware Pair Selection for Energy-Time Trade-Offs
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Solution Overview
Problem
Current approaches lack a systematic method to optimize energy consumption and time-to-solution trade-offs in the execution of solvers across different hardware platforms without compromising accuracy, particularly in scientific simulations and AI applications.
Innovation Solution
A computer-implemented method and system that identify non-dominated solver method-hardware platform pairs by measuring and plotting energy consumption and time-to-solution, providing an interface for user selection and adjusting hardware and software parameters to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If solver execution is optimized for minimum time-to-solution, then computational speed is improved, but energy consumption increases
Solution Approach 1:
The system dynamically selects solver methods and hardware platforms based on real-time energy consumption measurements and trade-off analysis. It adapts the solver configuration to balance between speed and energy efficiency, allowing the system to shift between performance-critical and energy-critical modes depending on operational context and constraints.
Solution Approach 2:
The invention changes key parameters including solver method selection, hardware platform choice, and execution configuration to optimize the balance between time-to-solution and energy consumption. By varying these parameters across different scenarios, the system achieves optimal trade-offs without sacrificing either speed or energy efficiency.
2Use of energy by moving object
If energy consumption is minimized, then operational cost is reduced, but time-to-solution increases
Solution Approach 1:
The system dynamically adjusts solver execution strategies based on energy constraints and time requirements. When energy minimization is the primary goal, the system selects energy-efficient solver methods and hardware configurations, accepting longer execution times. When time is critical, it switches to faster but more energy-intensive methods.
Solution Approach 2:
The invention modifies execution parameters such as precision level, solver algorithm selection, and hardware resource allocation to achieve energy-efficient execution. By changing these parameters, the system reduces energy consumption while providing users with control over acceptable time-to-solution trade-offs.
3Reliability
If solver accuracy is maintained at high level, then result reliability is improved, but computational resource consumption increases
Solution Approach 1:
The system changes precision parameters and solver configuration to achieve the required accuracy level with minimal computational resources. It selects appropriate precision levels (single vs. double precision) and adjusts solver tolerances to maintain reliability while reducing energy consumption, allowing flexible trade-offs based on problem requirements.
Solution Approach 2:
The invention applies different quality levels to different parts of the computation. It selectively uses high-precision methods only where critical for accuracy and lower-precision methods where approximate results are sufficient, optimizing the balance between reliability and energy consumption across the entire solver execution.
4Speed
If hardware platform is selected for maximum performance, then computing speed is improved, but energy efficiency deteriorates
Solution Approach 1:
The system provides a universal solver framework that can operate on multiple hardware platforms (CPUs, GPUs, FPGAs, ASICs) and automatically selects the optimal platform based on the balance between computing speed and energy efficiency. This multi-functional approach allows the same solver to adapt to different hardware characteristics and operational requirements.
Solution Approach 2:
The invention dynamically selects the optimal hardware platform based on real-time energy consumption measurements and workload characteristics. The system can switch between different hardware platforms depending on whether speed or energy efficiency is the primary constraint, providing flexible adaptation to varying operational conditions.
Data Source
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AI summary
Disclosed is a method (100) for managing energy consumption and time-to-solution in execution of solvers. The method includes executing a solver using a solver method on a hardware platform. The method further includes measuring energy consumption and time-to-solution of the solver method on the hardware platform during execution of the solver. The method further includes determining a set of non-dominated solver method-hardware platform pairs based on the measured energy consumption and time-to-solution. Herein, each non-dominated solver method-hardware platform pair represents a trade-off between minimizing energy consumption and minimizing time-to-solution without affecting accuracy of the solver. The method further includes configuring the hardware platform to execute the solver using one of the solver method-hardware platform pairs from the non-dominated solver method-hardware platform pairs.