Catalytic computing, a new theoretical framework, aims to overcome the limitations of traditional computing by leveraging the entire storage capacity of a device, such as a hard drive, for computation. Instead of relying on limited working memory, catalytic computing treats the entire memory system as a catalyst, allowing data to transform itself through local interactions within the storage itself. This approach, inspired by chemical catalysts, could drastically expand the complexity and scale of computations possible, potentially enabling the efficient processing of massive datasets that are currently intractable for conventional computers. While still theoretical, catalytic computing represents a fundamental shift in thinking about computation, promising to unlock the untapped potential of existing hardware.
A Brown University undergraduate, Noah Golowich, disproved a long-standing conjecture in data science related to the "Kadison-Singer problem." This problem, with implications for signal processing and quantum mechanics, asked about the possibility of extending certain "frame" functions while preserving their key properties. A 2013 proof showed this was possible in specific high dimensions, leading to the conjecture it was true for all higher dimensions. Golowich, building on recent mathematical tools, demonstrated a counterexample, proving the conjecture false and surprising experts in the field. His work, conducted under the mentorship of Assaf Naor, highlights the potential of exploring seemingly settled mathematical areas.
Hacker News users discussed the implications of the undergraduate's discovery, with some focusing on the surprising nature of such a significant advancement coming from an undergraduate researcher. Others questioned the practicality of the new algorithm given its computational complexity, highlighting the trade-off between statistical accuracy and computational feasibility. Several commenters also delved into the technical details of the conjecture and its proof, expressing interest in the specific mathematical techniques employed. There was also discussion regarding the potential applications of the research within various fields and the broader implications for data science and machine learning. A few users questioned the phrasing and framing in the original Quanta Magazine article, finding it slightly sensationalized.
A new algorithm for the "pancake sorting problem" — sorting a disordered stack by repeatedly flipping sections of it — has achieved near-optimal efficiency. While the minimal number of flips required to sort any stack remains unknown, the new algorithm, developed by researchers at MIT and other institutions, guarantees completion within 1.375 times the theoretical minimum. This represents a significant improvement over previous algorithms, edging closer to a perfect solution for a problem that has puzzled computer scientists for decades. The researchers employed a recursive strategy that breaks down large stacks into smaller, more manageable substacks, optimizing the flipping process and setting a new benchmark for pancake sorting efficiency.
Hacker News users discussed the practicality and significance of the new book-sorting algorithm. Some questioned the real-world applicability given the specialized constraints, like pre-sorted sections and a single robot arm. Others debated the definition of "perfection" in sorting, pointing out that minimizing the arm's travel distance might not be the only relevant metric. The algorithm's novelty and mathematical elegance were acknowledged, but skepticism remained about its potential impact beyond theoretical computer science. Several commenters highlighted the existing highly optimized solutions for real-world sorting problems and suggested that this new algorithm is more of an interesting theoretical exercise than a practical breakthrough. There was also discussion about the difference between this algorithm and existing techniques like Timsort, with some arguing the new algorithm addresses a distinctly different problem.
Summary of Comments ( 15 )
https://news.ycombinator.com/item?id=43091159
Hacker News users discussed the potential and limitations of catalytic computing. Some expressed skepticism about the practicality and scalability of the approach, questioning the overhead and energy costs involved in repeatedly reading and writing data. Others highlighted the potential benefits, particularly for applications involving massive datasets that don't fit in RAM, drawing parallels to memory mapping and virtual memory. Several commenters pointed out that the concept isn't entirely new, referencing existing techniques like using SSDs as swap space or leveraging database indexing. The discussion also touched upon the specific use cases where catalytic computing might be advantageous, like bioinformatics and large language models, while acknowledging the need for further research and development to overcome current limitations. A few commenters also delved into the theoretical underpinnings of the concept, comparing it to other computational models.
The Hacker News thread discussing the Quanta Magazine article "Catalytic computing taps the full power of a full hard drive" contains several interesting comments exploring the potential and limitations of the proposed catalytic computing paradigm.
Several commenters express excitement about the potential of catalytic computing to revolutionize data processing by enabling the use of all data stored on a hard drive simultaneously. They see this as a potential game-changer for fields dealing with massive datasets, like genomics and machine learning. The analogy to chemical reactions, where a catalyst facilitates a process without being consumed, is seen as a compelling and potentially fruitful way to rethink computation.
Some commenters delve into the technical aspects of the proposed system. One commenter questions the practical feasibility of achieving simultaneous access to all data on a hard drive, pointing out physical limitations like read/write head speed and data bus bandwidth. This leads to a discussion about the possible need for novel hardware architectures and data storage mechanisms to truly realize the vision of catalytic computing. Another comment explores the potential connection between catalytic computing and existing concepts like in-memory computing and distributed systems, suggesting that catalytic computing might represent a novel combination or extension of these ideas.
A few commenters express skepticism about the scalability and practicality of the proposed approach. They raise concerns about the potential energy consumption of such a system, particularly if it involves simultaneous access to all data on a large hard drive. The potential for noise and interference in a system with so many simultaneous operations is also mentioned as a potential challenge.
There's also a discussion about the potential applications of catalytic computing beyond the examples mentioned in the article. One commenter suggests its potential use in cryptography, particularly for breaking current encryption methods. Another commenter speculates on its application in areas like artificial intelligence and drug discovery.
Finally, some commenters express a desire for more technical details about the proposed catalytic computing system. They request more information about the specific mechanisms for data access, the nature of the "catalysts," and the expected performance characteristics of such a system. They suggest that a deeper understanding of these technical details is essential for assessing the true potential and limitations of catalytic computing.