Efficient Persistent Multi-Word Compare-and-Swap Algorithms for High-Performance Many-Core CPUs

Keskeiset käsitteet

The authors propose new persistent multi-word compare-and-swap (PMwCAS) algorithms that remove redundant compare-and-swap and cache flush instructions, improving performance on many-core CPUs compared to the original algorithm.

Tiivistelmä

The paper introduces new PMwCAS algorithms for many-core CPUs. The original PMwCAS algorithm by Wang et al. contains redundant compare-and-swap (CAS) and cache flush instructions, leading to performance degradation on many-core CPUs.

The authors propose two new PMwCAS algorithms:

1. PMwCAS with dirty flags: This algorithm uses dirty flags to indicate when target words are visible in CPU caches but not yet persisted. Worker threads must wait for words to be flushed before continuing their processes.
2. PMwCAS without dirty flags: This algorithm removes the dirty flags, using the PMwCAS descriptors as write-ahead logs instead to ensure consistency.

The key improvements in the proposed algorithms are:

• Removal of redundant CAS and flush instructions
• Exclusion of dirty flags, which helps avoid frequent small writes and double flushes

Experimental results show that the proposed methods are up to 10 times faster than the original PMwCAS algorithm. The authors also provide suggestions for using PMwCAS operations effectively, such as keeping the number of target words small, avoiding false sharing in CPU cache lines, and swapping high-contention words first.

Tilastot

The original PMwCAS algorithm has a throughput of only around 2 MOps/s on 56 threads, while the proposed methods achieve up to 20 MOps/s.
The 99th percentile latency of the original PMwCAS is around 1000 us, while the proposed methods achieve around 100 us.

Lainaukset

"Wang et al. proposed persistent multi-word compare-and-swap (PMwCAS) operations to support programming in persistent memory."
"We remove redundant CAS and flush instructions in our PMwCAS algorithms. In addition to the improvements applied to our MwCAS operations [23], we exclude dirty flags that manage data durability in the original algorithm."
"Experimental results demonstrate the effectiveness of the proposed methods. Our PMwCAS algorithms are up to ten times faster than the original algorithm."

Tärkeimmät oivallukset

Practical Persistent Multi-Word Compare-and-Swap Algorithms for Many-Core CPUs

by Kento Sugiur... klo arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01710.pdf

Syvällisempiä Kysymyksiä

How can the proposed PMwCAS algorithms be integrated into higher-level data structures and applications to fully leverage their performance benefits

The proposed PMwCAS algorithms can be integrated into higher-level data structures and applications to fully leverage their performance benefits by following a few key strategies:

Data Structure Integration: Incorporating the PMwCAS operations into data structures like B+-trees, hash tables, and indexes can enhance their performance in persistent memory environments. By replacing traditional CAS operations with PMwCAS, these data structures can achieve atomic and durable updates across multiple words simultaneously, improving efficiency and consistency.

Transaction Processing: Integrating PMwCAS operations into transactional systems can enhance the atomicity, consistency, isolation, and durability (ACID) properties of the transactions. By leveraging PMwCAS for multi-word updates within transactions, developers can ensure that complex operations maintain data integrity and durability in persistent memory.

Concurrency Control: Utilizing PMwCAS algorithms in concurrent data structures can enhance their scalability and performance. By enabling lock-free or wait-free operations with PMwCAS, developers can reduce contention and improve throughput in multi-threaded applications accessing persistent memory.

Error Recovery: Leveraging PMwCAS descriptors as write-ahead logs can facilitate efficient error recovery mechanisms in applications. By using the information stored in the descriptors, applications can roll back or forward operations to maintain consistency in case of failures, ensuring data integrity and recoverability.

Optimizing Critical Sections: Identifying critical sections in applications where PMwCAS operations can be most beneficial and optimizing their usage can maximize the performance benefits. By strategically applying PMwCAS in areas with high contention or frequent multi-word updates, developers can achieve significant performance improvements.

By integrating PMwCAS algorithms thoughtfully into higher-level data structures and applications, developers can harness their capabilities to enhance performance, scalability, and reliability in persistent memory programming.

What are the potential trade-offs or limitations of the approach of using PMwCAS descriptors as write-ahead logs instead of dirty flags

The approach of using PMwCAS descriptors as write-ahead logs instead of dirty flags offers several advantages but also comes with potential trade-offs and limitations:
Trade-offs:

Performance Overhead: While avoiding the use of dirty flags can reduce the number of flush instructions and cache invalidations, the additional complexity of managing PMwCAS descriptors as write-ahead logs may introduce overhead in terms of processing and memory usage.

Recovery Complexity: Relying solely on PMwCAS descriptors for recovery may increase the complexity of error recovery mechanisms. Ensuring the correct roll-back or roll-forward operations based on the information stored in descriptors requires careful implementation and error-handling logic.

Limitations:

Consistency Guarantees: Depending solely on PMwCAS descriptors for ensuring data consistency may have limitations in certain scenarios. Handling complex data structures or operations that require additional mechanisms for maintaining consistency could pose challenges with this approach.

Scalability Concerns: As the complexity of applications and data structures increases, managing PMwCAS descriptors for all operations may become cumbersome and impact scalability. Balancing the benefits of using descriptors with the overhead they introduce is crucial for optimal performance.

Resource Utilization: Storing and managing PMwCAS descriptors for every operation can consume additional memory and computational resources. Optimizing the utilization of descriptors while maintaining performance gains is essential.

While using PMwCAS descriptors as write-ahead logs offers benefits in terms of performance and durability, developers need to carefully consider the trade-offs and limitations to ensure effective integration into their applications.

Can the insights and techniques from this work be applied to improve the performance of other persistent memory programming primitives beyond just PMwCAS operations

The insights and techniques from this work on PMwCAS algorithms can be applied to improve the performance of other persistent memory programming primitives beyond just PMwCAS operations in the following ways:

Optimizing Atomic Operations: Techniques for reducing redundant CAS and flush instructions, as demonstrated in the PMwCAS algorithms, can be applied to other atomic operations in persistent memory programming. By streamlining the execution of multi-word updates and minimizing unnecessary operations, the performance of various atomic primitives can be enhanced.

Enhancing Transactional Systems: The concept of using PMwCAS descriptors as write-ahead logs can be extended to improve the durability and recoverability of transactions in persistent memory systems. By leveraging similar mechanisms for maintaining transactional consistency and durability, developers can enhance the reliability of transaction processing in persistent memory environments.

Concurrency Control Mechanisms: Applying the principles of PMwCAS algorithms to concurrency control mechanisms can optimize the performance of lock-free and wait-free data structures. By incorporating efficient multi-word compare-and-swap techniques and leveraging descriptors for managing concurrent operations, developers can enhance the scalability and efficiency of concurrent programming in persistent memory.

Error Recovery Strategies: Leveraging the approach of using PMwCAS descriptors for error recovery can be beneficial for various persistent memory programming primitives. By implementing robust recovery mechanisms based on write-ahead logs, developers can ensure data consistency and integrity across different persistent memory operations, improving the reliability of the overall system.

By extrapolating the insights and techniques from PMwCAS algorithms to other persistent memory programming primitives, developers can enhance the performance, reliability, and efficiency of a wide range of applications and data structures in persistent memory environments.