Talk:Gustafson's law

message: In the high-performance computing community, scaling according to  Amdahl's Law, i.e. under the assumption of fixing the total problem size, is commonly referred to as "strong" scaling. Conversely, scaling under conditions where the problem size per computing unit is kept constant, is referred to as "weak" scaling. Depending on the application and use-case at hand, the developer/user might aim to increase strong or weak scalability of the code of interest.     — Preceding unsigned comment added by 141.58.5.7 (talk) 07:00, 29 August 2012 (UTC)
thread_title:  Strong vs. weak scaling 

message: Since Shi's manuscript is unpublished, Wikipedia cannot make statements about it being the first to point out something. AshtonBenson (talk) 18:32, 8 November 2009 (UTC)
thread_title:  Priority for Shi 

message: "The impact of the law was the shift in research" --which law?  Amdahl's or Gustafson's? 24.124.29.17 (talk) 14:52, 7 April 2009 (UTC)
thread_title:  Other 

message: I think using the term "speedup" here, without differentiating it from its use in Amdahl's law, is dangerously misleading.
Gustafson clearly shows in his paper that he does NOT use the same definition of speedup as Amdahl: he introduces the term "scaled speedup" to refer to his definition, the difference being -- as I understand it -- that:
* "speedup" as used by Amdahl refers to the decrease in computation time for a parallelized application compared to a sequential one -- the reference is the sequential performance
* "scaled speedup" as defined by Gustafson refers to the inverse of the increase in computation time for a sequential application compared to a parallel one -- the reference is the parallel performance
See fig. 2a & 2b in Gustafson's paper if you prefer to visualise things (I don't know if these figures can be used in the Wiki page?).
Also, IMO Gustafson's law does not contradict Amdahl's law: it's merely a different approach to parallelism, but both are valid.
As explained in the last paragraph of Gustafson's paper, Gustafson's point has to be replaced in the context of a misuse of Amdahl's law causing overrated skepticism over the practical value of massive parallelism (quoting Gustafson, a "mental block" in the research community).
I'm just pointing out things and not actually changing anything because I'm not an expert in the field, and above all I'm not an expert Wikipedian. However if someone more experienced deems what I said worthy of inclusion in the Wiki page, feel free to proceed -- or to confirm that I should proceed -- and update the page.  — Preceding unsigned comment added by Laomai Weng (talk • contribs) 20:04, 15 April 2011 (UTC)
thread_title:  Scaled speedup 

message: We may assume that the sequntial time is T:=P. Thus
:T = (1-\alpha)\cdot P + \alpha\cdot P
It follows that:
:t = (1-\alpha) + \alpha\cdot P = \alpha\cdot(P-1)+1
hence the speedup is:
:\frac Tt = \frac P{\alpha\cdot(P-1)+1}
Wlod (talk)  —Preceding undated comment added 09:13, 31 January 2012 (UTC)
thread_title:  Still a bit simpler derivation 

message: In the article \alpha stands for the proportion of sequential time during the process in which the parallel portion is performed by P processors. Thus actually it's not just a constant \alpha but a function \alpha:=\alpha(P). However it was natural for me (perhaps for many readers who care at all?) that \alpha was a fraction of the time spent on the sequential part when the whole computation is done sequentially--then indeed \alpha is a constant (at least with respect to P) Wlod (talk) 02:03, 10 February 2012 (UTC)
thread_title:  Two interpretations (the cause behind the confusion) 

message: Among researchers in parallel computing, there seems to be a lack of understanding of computational complexity.  Over and over again, we see people ditching a serial O(n log n) algorithm for a parallelizable O(n squared) algorithm because they can show greater speedup, relative to the WRONG BASELINE.  For instance, Bellman-Ford is preferable to Dijktra's ONLY if you have negative weights in a graph.  Otherwise, for even moderate problem sizes, Dijkstra's will always be faster on a single processor than Bellman-Ford on a supercomputer.  See Dispelling the Myths of Parallel Computing and also Twelve Ways to Fool the Masses When Giving Performance Results on Parallel Computers.  Sure, there are lots of embarrassingly or sufficiently parallel problems, but researchers are fixated on toy problems that are not parallelizable in any way that has intellectual merit.  — Preceding unsigned comment added by 69.204.203.196 (talk) 12:48, 20 September 2014 (UTC)
thread_title:  Pitfalls of "scaling up the problem" 

message: "Lump of labor fallacy is the contention that the amount of work available to labourers is fixed."  — Preceding unsigned comment added by Intellec7 (talk • contribs) 17:47, 20 December 2015 (UTC)
replies: text: Somehow. The basis of Gustafson's theory (calling anything as obvious a law is ridiculous) is the fact with more workers you can do more refined jobs, which would indeed fit our actual civilization, made of so many constructions purposed to underpin previous ones, allowing for a nation-wide Ponzi system (potentially able?) to fake an actual government by only basing it on (relatively) small scale companies I/O's, should they have real purposes or not (encoded government-directed management data in operation results, employees number, wages, sales, capital itself...). Big data has similar individuals' population profiling implications, by the way. Realistically, the example given is one of the worst ideas anyone could ever have, for the simplest of all reasons... this requires everyone to move from home to a distant working place, which at our population level and because of how politics have been done in the recent past means excessive energy use in transports leading to pollution (not only gaseous, asphalt and over-concentrated cities are too : try to dispatch your nation's population across the whole territory, it's simply disgusting here and actually this doesn't give us any benefit) and other non-obvious but evident consequences. Note that this kind of computer-driveable government could be considered democratic, as everyone would contribute to diverse cross-regulations (truly dependent people aside), which by itself is such a joke I can't stop contemplating the (totally stupid) idea. --2A01:CB11:13:D700:D186:445D:41E4:DA1D (talk) 13:26, 24 October 2018 (UTC)
thread_title:  related to Lump of Labor Fallacy? 

message: The article is terribly confusing: "speedup in latency of the execution of a task at fixed execution time". What is a "speedup in latency" if we are assuming the total execution time is fixed?  Jess_Riedel (talk) 17:07, 1 November 2020 (UTC)
thread_title:  Confusing  

message: The article currently reads:
> The execution time on the serial system is: T' = s + Np
which does not make any sense, because for a serial system N=1. Also, N typically designates the problem size, not the number of processors. Kosarev7 (talk) 21:21, 31 October 2023 (UTC)
thread_title:  Serial runtime is wrong