Software Profiling Using Segmented Buffer Architecture
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Solution Overview
Problem
Traditional software profiling methods face challenges such as significant system overhead, scalability issues, and vulnerability to denial of service attacks due to the need for large pinned buffers, which affect accuracy and resource usage.
Innovation Solution
A system and method using a small pinned buffer, typically 400 bytes or less, to store program counters, which is periodically updated to a larger non-pinned histogram buffer, reducing system resource usage and vulnerability while maintaining accuracy and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large histogram buffer is pinned in memory for kernel-based profiling, then profiling accuracy is maintained, but system vulnerability to denial of service attacks increases and scalability deteriorates
Solution Approach 1:
The patent divides the profiling system into two buffer components: a small pinned buffer (400 bytes or less) that remains in kernel space for security, and a large non-pinned buffer that resides in user space for storing actual histogram data. This segmentation allows the system to maintain profiling accuracy while eliminating the security vulnerability of pinning large buffers in kernel space.
Solution Approach 2:
The patent introduces a signal handler as an intermediary mechanism that transfers data from the small pinned buffer to the large non-pinned buffer. The signal handler processes program counter samples asynchronously, allowing the kernel to maintain a minimal pinned buffer while user space handles the bulk of data storage and processing, thus resolving the security-scalability contradiction.
2Measurement precision
If a large histogram buffer is pinned in memory, then profiling accuracy is maintained, but system resource usage increases
Solution Approach 1:
The patent segments memory allocation between kernel and user space, assigning only a small pinned buffer (400 bytes or less) to the kernel while allocating the large non-pinned buffer in user space. This segmentation dramatically reduces kernel memory consumption while preserving the ability to store comprehensive profiling data in user space.
Solution Approach 2:
The patent enables the user space signal handler to autonomously manage the large non-pinned buffer, performing self-service functions including data reception from the pinned buffer, histogram computation, and buffer management. This self-service approach eliminates the need for the kernel to allocate or manage large buffers, reducing system resource usage.
3Quantity of substance
If a large histogram buffer is pinned in memory, then profiling data can be stored, but scalability to multiple threads deteriorates
Solution Approach 1:
The patent segments buffer management by thread, allowing each thread to have its own small pinned buffer in kernel space and associated large non-pinned buffer in user space. This per-thread segmentation enables scalable thread-level profiling without requiring each thread to pin a large buffer in kernel space, thus improving adaptability while maintaining buffer capacity.
4Measurement precision
If kernel-based profiling with interrupt handling is used, then profiling accuracy is maintained, but system overhead increases
Solution Approach 1:
The patent implements periodic action by using signal handlers that are triggered at scheduled intervals rather than continuous interrupt handling. The signal handler processes program counter samples asynchronously at periodic intervals, reducing the frequency and impact of kernel interruptions while maintaining profiling accuracy through systematic sampling.
Solution Approach 2:
The patent introduces a signal handler as an intermediary that decouples the kernel's interrupt handling from the actual profiling data processing. The kernel's role is reduced to writing program counters to the small pinned buffer and triggering signals, while the signal handler in user space performs the computationally intensive histogram updates, thereby reducing kernel overhead and system time loss.
Data Source
AI summary
A method for profiling software programs uses a small pinned buffer and a large unpinned histogram buffer. When a process that is being profiled receives a time slice, the process' program counter is written to the small pinned buffer by a kernel routine. The small pinned buffer is configured to be large enough to store several program counters. When the small pinned buffer is full, or almost full, an internal profiling signal is sent by the kernel routine. When the process is resumed, any outstanding signals (including the profiling signal) are processed before resuming the process. The profiling signal is handled by reading the program counters from the small pinned buffer, calculating a slot in the histogram buffer that corresponds to each of the program counters, incrementing the value in the corresponding histogram slots, and clearing the small pinned buffer so that it can be reused.