How to make things slower so they go faster

The discussion revolves around several key themes, primarily concerning the relationship between speed, efficiency, and system performance, often in the context of computing and resource management. Users draw parallels to various real-world scenarios and well-known concepts to illustrate these ideas, while also critiquing the accessibility of mathematical explanations for complex systems.

Accessibility of Mathematical Explanations

A significant portion of the conversation focuses on the readability and effectiveness of mathematical explanations for complex, probabilistic systems. Several users found the original post's reliance on mathematical formulas to be a barrier to understanding.

• SpaceManNabs suggested that "the math doesn't read well" and that paragraphs dealing with probabilistic calculations could have been better illustrated with mathematical lines rather than predominantly linguistic explanations. They also acknowledged the need for math in probabilistic approaches but questioned its accessibility, suggesting "a hard threshold calculation is more accessible and maybe just as good."
• cogman10 echoed this sentiment, stating, "For something like this, annotated graphs and examples (IMO) work a lot better than formulas in explaining the problem and solution." They further elaborated that distributed computer systems are not purely mathematical problems, as "Load often comes from usage which is often closer to random inputs rather than predicable variables."

The Counter-Intuitive Nature of "Slow is Smooth, Smooth is Fast"

Several users discussed the adage "Slow is smooth, and smooth is fast," noting its prevalence across various fields and its counter-intuitive nature, especially for beginners. This phrase is interpreted as a principle where deliberate, careful execution leads to better overall efficiency and fewer errors, ultimately resulting in faster progress than attempting to rush.

• yardshop brought up the saying, noting its usage in construction videos and explaining that "Going slowly and being careful leads to fewer mistakes, which will be a ‘smoother’ process and ends up taking less time, whereas going too fast and making mistakes means work has to be redone and ultimately takes longer." They also drew a parallel to processing and mental queues: "When one is trying to go too fast, and is possibly becoming impatient with their progress, their mental queue fills up and processing suffers. If one accepts a slower pace, one's natural single-tasking capability will work better, and they will make better progress as a result."
• anonymars offered a simple analogy: "Kind of like the tortoise and the hare?"
• bluedino and c0nsumer mentioned its commonality in auto racing and mountain biking, respectively, as a way to counter beginners' tendencies to rush and crash.
• eszed provided a more detailed explanation from the context of auto racing, citing Bill Miliken's Race Car Vehicle Dynamics. They explained that "going slow(er) into a corner allows you to hit the apex precisely, optimally rotate the car, and get on the power sooner, which gets you a higher exit speed."
• wallflower found this principle particularly relevant to learning musical instruments, observing that "the hardest part of learning to play a musical instrument is the tendency to want to play at normal speed before you are ready." They quoted the adage, “You don’t rise your level when performing. You fall to your level of practice.”

However, JasonSage offered a counterpoint, suggesting the saying can be interpreted differently in sports contexts: "You practice at an uncomfortable pace to normalize it, even making mistakes, because if you can’t practice at game speed you won’t be able to compete at game speed." They also argued for a balance: "In that context there’s room for both, and I’d say the same for music—you need slow, deliberate practice and also reps in ‘performance’ mode."

• milesvp shared a compelling anecdote about an organizational level application of this principle: "We essentially decided to slow down. ... our velocity basically went up." They noted that "just promising fewer deliverables allowed us to deliver more," leading to increased velocity and average value of work.

Capacity Management and Load Balancing

Several comments touched upon practical aspects of managing system capacity, dealing with synchronized demand, and optimizing resource utilization.

• evaXhill referenced Facebook's "Asynchronous computing @Facebook" post, highlighting concepts like "capacity optimization (queuing + time shifting)" and "capacity regulation along with user delay tolerance."
• bluedino mentioned the practicalities of managing jobs based on resource availability, suggesting methods like "stagger[ing] the jobs so only a couple of them hit the disks at a time" and measuring performance to identify bottlenecks.
• mparnisari, in a critical note about the article's clarity, quoted an excerpt about "Synchronized demand" and expressed confusion.
• cstrahan provided a helpful explanation of the quoted phrases, likening "usable headroom" to "wiggle room" and defining synchronized demand with examples like a timezone-based surge in activity or automated retries after an outage.

Parallelism and Pipelining in Compute

A few users highlighted the increasing importance of parallelism and pipelining in modern computing architectures.

• mikewarot discussed the potential of spreading computations across hardware like FPGAs, mentioning a trade-off between latency and throughput. They stated, "parallelism and pipelining are the future of compute."

System Dynamics and Counter-Intuitive Outcomes

The discussion also touched on phenomena where system design choices can lead to unexpected or counter-intuitive performance outcomes.

• taeric brought up "Braess's paradox," asking if it was a "contrary wording of this? How making things faster slows things down?" They linked to videos explaining this concept.
• mhb provided a definition of Braess's Paradox: "the observation that adding one or more roads to a road network can slow down overall traffic flow through it" and linked to its Wikipedia page.

Low-Level Implementation and Control

One user described a practical, portable approach to managing resource usage in long-running processes.

• jmclnx described a method using nanosleep(2) based on data processed, controlled by a parameter file that could be adjusted during runtime. They mentioned handling cancel signals for restart capabilities. This highlights a more direct, low-level form of performance control.