Computational Storage Profiling for eBPF Execution
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Solution Overview
Problem
Existing computational storage devices (CSDs) using Berkeley Packet Filter (BPF) face inefficiencies due to suboptimal performance when translating eBPF instructions to modern embedded processors like ARM and RISC-V, leading to slower execution and reduced performance in computational storage applications.
Innovation Solution
Implementing a profiling system within the CSD to measure and optimize the execution parameters of computational instruction sets, such as eBPF, by translating them into native instruction sets and monitoring execution to generate logs of performance metrics, which can be communicated to a host for analysis and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If eBPF instructions are translated to native instruction sets on modern embedded processors, then computational storage devices can execute BPF programs, but execution performance becomes suboptimal and slower
Solution Approach 1:
The patent creates a copy of the eBPF interpreter functionality by implementing a profiling component that emulates the instruction execution process. This allows performance measurement without requiring actual translation to native instructions, thus avoiding the performance penalty while maintaining execution capability analysis.
Solution Approach 2:
The profiling component acts as an intermediary between the eBPF instruction set and the native processor. It intercepts and monitors instruction execution, measuring performance metrics without requiring full translation to native code, thereby preserving execution speed while enabling performance analysis.
2Productivity
If profiling is implemented to measure execution parameters, then performance optimization is enabled, but device complexity increases
Solution Approach 1:
The profiling component is designed to be universal, handling multiple eBPF instruction types and performance metrics through a single integrated mechanism. This multi-functionality reduces the need for separate profiling logic for each instruction type, thereby limiting the increase in device complexity while maintaining comprehensive performance measurement capability.
3Measurement precision
If execution logs are generated and communicated to host, then performance analysis is improved, but bandwidth usage increases
Solution Approach 1:
The patent extracts only the essential performance metrics from the execution logs and transmits them to the host system. By filtering and selecting only the most relevant performance data, the system maintains high measurement precision while minimizing the amount of data transmitted, thus reducing bandwidth consumption and energy loss.
Data Source
AI summary
The technology disclosed herein pertains to a system and method for profiling performance of an embedded computation instruction set (CIS), the method including receiving a profiling component to a computational storage device (CSD), the profiling component being configured to measure one or more execution parameters of a computational instruction set (CIS), executing the CIS at a program slot in a computational storage processor of the CSD, monitoring the execution of the CIS to generate a log of the execution parameters of the CIS, and communicating the log to a host in response to receiving a get-log page command.