NUMA-Aware Statistics Counters for Scalable Multicore Systems
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Solution Overview
Problem
In multicore architectures with Non-Uniform Memory Access (NUMA) systems, shared statistics counters face scalability bottlenecks and inaccuracies due to contention between threads, leading to increased latency and loss of updates, especially in systems with distributed cache-coherent NUMA (CC-NUMA) properties.
Innovation Solution
Implementing scalable statistics counters by splitting counters into multiple components, using probabilistic counters, and employing adaptive techniques such as node affinity and randomized backoff to reduce contention, allowing consecutive updates on a single node and minimizing cache misses, while maintaining accuracy and low space overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If shared statistics counters are used in NUMA systems, then performance monitoring and diagnostics are enabled, but scalability bottlenecks and latency increase due to invalidation traffic on every modification
Solution Approach 1:
The patent divides the shared counter into multiple per-thread counters, each maintained locally by a thread. This segmentation eliminates the need for invalidation traffic when threads update their local counters, while still enabling accurate aggregate statistics collection by summing individual thread counters when needed.
Solution Approach 2:
Each thread maintains its own local counter with full update privileges, eliminating remote access latency and invalidation traffic. The local counter provides the same functional capability as a shared counter but with superior performance characteristics for the local thread.
2Reliability
If naive concurrent counter implementations are used, then thread-safe counting is achieved, but performance degrades due to contention and invalidation traffic as thread counts grow
Solution Approach 1:
The counter is segmented into independent per-thread counters, eliminating contention between threads. Each thread safely updates only its own counter without requiring synchronization or invalidation protocols, achieving both thread-safety and high performance under contention.
Solution Approach 2:
Each thread independently maintains and updates its own counter without requiring services from other threads or centralized coordination. This self-service approach eliminates the need for invalidation traffic and synchronization overhead while maintaining thread-safety.
3Productivity
If updates are moved outside transactions to reduce contention, then transactional success rates improve, but the semantics of the program are changed
Solution Approach 1:
By segmenting the counter into per-thread counters, the patent allows updates to remain within transactions without causing contention. Each thread's update to its own counter is independent and does not conflict with other threads, maintaining both transactional success rates and original program semantics.
Data Source
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AI summary
The systems and methods described herein may be used to implement scalable statistics counters suitable for use in systems that employ a NUMA style memory architecture. The counters may be implemented as data structures that include a count value portion and a node identifier portion. The counters may be accessible within transactions. The node identifier portion may identify a node on which a thread that most recently incremented the counter was executing or one on which a thread that has requested priority to increment the shared counter was executing. Threads executing on identified nodes may have higher priority to increment the counter than other threads. Threads executing on other nodes may delay their attempts to increment the counter, thus encouraging consecutive updates from threads on a single node. Impatient threads may attempt to update the node identifier portion or may update an anti-starvation variable to indicate a request for priority.