Algorithms for the 21st Century (2006)
Stephen C. Johnson's paper critiques traditional algorithms, emphasizing the need for reevaluation to optimize performance on modern hardware, advocating for sequential memory access and collaboration between software and hardware developers.
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The paper "Algorithms for the 21st Century" by Stephen C. Johnson critiques the traditional algorithms taught in computer science, arguing that they do not align with the performance characteristics of modern computing machines. It highlights that while algorithms are often analyzed based on a uniform memory model, contemporary systems reward sequential memory access rather than locality of reference. This discrepancy is particularly significant for large problems that necessitate 64-bit architectures. The author emphasizes the importance of understanding how CPU cycle counts can provide accurate performance measurements, although they can be influenced by various factors such as system load and hardware configurations. Through experiments, the paper demonstrates that the order of operations in summing large arrays can significantly impact performance, with sequential access proving to be more efficient than accessing elements in a non-linear fashion. The findings suggest that traditional data structures and algorithms may not be optimal for modern hardware, especially as data sizes grow beyond cache capacities. The author calls for a reevaluation of algorithm design principles, advocating for a trial-and-error approach to optimize performance for large data applications. The paper concludes by suggesting collaboration between software and hardware developers to create more efficient algorithms and data structures tailored to contemporary computing environments.
- Traditional algorithms may not perform well on modern hardware.
- Sequential memory access is favored over locality of reference in current systems.
- Performance can vary significantly based on the order of operations in algorithms.
- A trial-and-error approach may be necessary for optimizing algorithms for large data.
- Collaboration between software and hardware developers is essential for future algorithm design.
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5 commentsIn my humble opinion as a volunteer educator, algorithms are already very complicated and students being introduced to them don’t need to concern themselves with this stuff at the same time.
I just spent a week shaving 98% off of the latency in a single RPC call by going from O(N^2) to O(1). Data locality is not a problem I have. :)
I wonder whether an optimizing compiler can detect and optimize the sequential writes of the same values, possibly by replacing them with something like a blitter. It would be interesting to see the difference in speed if the data to be written was random, rather than the same value (1.0) every time.
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