- Sep 2024
- Dec 2023
-
superfastpython.com superfastpython.com
-
There are 5 ways to measure execution time manually in Python using the time module, they are:
- Use
time.time() - Use
time.perf_counter() - Use
time.monotonic() - Use
time.process_time() - Use
time.thread_time()
Note, each function returns a time in seconds and has an equivalent function that returns the time in nanoseconds, e.g.
time.time_ns(),time.perf_counter_ns(),time.monotonic_ns(),time.process_time_ns()andtime.thread_time_ns().Recall that there are 1,000 nanoseconds in one microsecond, 1,000 microseconds in 1 millisecond, and 1,000 milliseconds in one second. This highlights that the nanosecond versions of the function are for measuring very short time scales indeed.
- Use
-
- Nov 2023
-
firefox-source-docs.mozilla.org firefox-source-docs.mozilla.org
-
Intel processors also support multiple P-states. P0 is the state where the processor is operating at maximum frequency and voltage, and higher-numbered P-states operate at a lower frequency and voltage to reduce power consumption. Processors can have dozens of P-states, but the transitions are controlled by the hardware and OS and so P-states are of less interest to application developers than C-states.
These exist too, but we can only control them indirectly at best.
-
Intel processors have aggressive power-saving features. The first is the ability to switch frequently (thousands of times per second) between active and idle states, and there are actually several different kinds of idle states. These different states are called C-states. C0 is the active/busy state, where instructions are being executed. The other states have higher numbers and reflect increasing deeper idle states. The deeper an idle state is, the less power it uses, but the longer it takes to wake up from.
Mental note: Think "C for the CPU cool down"
-
- Aug 2023
-
profiler.firefox.com profiler.firefox.com
- Jul 2023
-
-
Go users can create their custom profiles via pprof.Profile and use the existing tools
do we have any custom implementations of these profiles available publicly
-
- Nov 2022
-
www.causal.app www.causal.app
-
- Sep 2022
-
-
a benchmark tells you how slow your code is ("it took 20 seconds to do X Y Z") and a profiler tells you why it's slow ("35% of that time was spent doing compression").
-
-
rbspy.github.io rbspy.github.io
-
Because rbspy is a sampling profiler (not a tracing profiler), it actually can't tell you how times a function was called -- it just reports "hey, I observed your program 100,000 times, and 98,000 of those times it was in the calculate_thing function". ruby-prof is a tracing profiler for Ruby, which can tell you exactly how many times each function was called at the cost of being higher overhead.
-
it's useful to understand the difference between "self time" and "total time" spent in a function
-
-
rbspy.github.io rbspy.github.io
-
Nothing in the profiling guide is Ruby- or rbspy-specific — it all applies to profiling in general.
-
-
stackoverflow.com stackoverflow.com
-
That is called profiling, not performance testing. Performance testing should ensure that a piece of code runs within a desired amount of time, given a certain context, before the new code goes into production.
-
- Aug 2022
-
blog.flycode.com blog.flycode.com
- Jun 2022
-
wicg.github.io wicg.github.io
Tags
Annotators
URL
-
-
www.youtube.com www.youtube.com
-
-
developer.chrome.com developer.chrome.com
-
alexsidorenko.com alexsidorenko.com
-
alexsidorenko.com alexsidorenko.com
Tags
Annotators
URL
-
- May 2022
- Apr 2022
-
www.science.org www.science.org
-
Mathew, D., Giles, J. R., Baxter, A. E., Oldridge, D. A., Greenplate, A. R., Wu, J. E., Alanio, C., Kuri-Cervantes, L., Pampena, M. B., D’Andrea, K., Manne, S., Chen, Z., Huang, Y. J., Reilly, J. P., Weisman, A. R., Ittner, C. A. G., Kuthuru, O., Dougherty, J., Nzingha, K., … Wherry, E. J. (2020). Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications. Science, 369(6508), eabc8511. https://doi.org/10.1126/science.abc8511
-
- Feb 2022
-
www.sciencedirect.com www.sciencedirect.com
-
learner profiling
unsupervised machine learning technique
-
- Dec 2021
- Jul 2021
-
www.migrationencounters.org www.migrationencounters.org
-
Claudia: How long did you live in the States?Yosell: Let's see, about 24 years. Out here in Mexico, I've probably been here for like a year and a half. Just barely, I guess.Claudia: What was it like coming back to Mexico? You said you made the decision on your own?Yosell: Yeah, I mean, I already did know about it just a little bit, so it wasn't too bad. It was just basically like Los Angeles, it's the same thing, really. Just the differences, the corruption out here, and how people treat you. I would probably walk down the street, and I would always get a dirty look or something. I'd always get checked by the cops here, that's a constant thing for me.
Reflections
Tags
Annotators
URL
-
- Oct 2020
-
github.com github.com
-
engineering.appfolio.com engineering.appfolio.com
-
optcarrot, is a headless NES emulator that the Ruby core team are using as a CPU-intensive optimization target.
-
- Sep 2020
-
www.thelancet.com www.thelancet.com
-
Chu, H., Chan, J. F.-W., Yuen, T. T.-T., Shuai, H., Yuan, S., Wang, Y., Hu, B., Yip, C. C.-Y., Tsang, J. O.-L., Huang, X., Chai, Y., Yang, D., Hou, Y., Chik, K. K.-H., Zhang, X., Fung, A. Y.-F., Tsoi, H.-W., Cai, J.-P., Chan, W.-M., … Yuen, K.-Y. (2020). Comparative tropism, replication kinetics, and cell damage profiling of SARS-CoV-2 and SARS-CoV with implications for clinical manifestations, transmissibility, and laboratory studies of COVID-19: An observational study. The Lancet Microbe, 1(1), e14–e23. https://doi.org/10.1016/S2666-5247(20)30004-5
-
- Jul 2020
-
github.com github.com
-
Looks like this alternative is better maintained: https://github.com/test-prof/test-prof
Meat: https://github.com/sinisterchipmunk/rspec-prof/blob/master/lib/rspec-prof.rb
-
-
ruby-prof.github.io ruby-prof.github.io
-
ruby-prof supports excluding specific methods and threads from profiling results. This is useful for reducing connectivity in the call graph, making it easier to identify the source of performance problems when using a graph printer. For example, consider Integer#times: it's hardly ever useful to know how much time is spent in the method itself. We are more interested in how much the passed in block contributes to the time spent in the method which contains the Integer#times call. The effect on collected metrics are identical to eliminating methods from the profiling result in a post process step.
Tags
Annotators
URL
-
-
www.nbcnews.com www.nbcnews.com
-
Video shows police drawing guns, handcuffing black man in his own home after false alarm
Man handcuffed in his own home.
-
- May 2020
-
www.iubenda.com www.iubenda.com
-
It’s important to note that where the GDPR applies, intended use factors into whether or not consent is required as even statistical data can fall under “profiling” or “monitoring” depending on how the data is being used.
-
-
www.iubenda.com www.iubenda.com
-
If you profile your users, you have to tell them. Therefore, you must pick the relevant clause from the privacy policy generator.
-
In case you’re implementing any ADM process, you have to tell your users.
-
If you’re selling products and keep record of users’ choices for marketing purposes, dividing them into meaningful categories, such as by age, gender, geographical origin etc., you’re profiling them.
-
-
gdpr-info.eu gdpr-info.eu
-
Tags
Annotators
URL
-
- Jan 2019
-
www.youtube.com www.youtube.com
-
- Oct 2017
-
Local file Local file
-
‘themorepredictableresultwouldbeagradualdesertificationoftheculturallifeofindividualsnolongerabletoencounterwhatisunusual,unexpected,andsurprising.’[61]Ratherthanindividualizedbubbles,sharingsegregatessocialnetworkusersintoculturalbubblesofpreferences,products,andknowledge
-
platformssuchasGoogleandFacebookthatoperatelike‘predictionengines’by‘constantlycreatingandrefiningatheoryofwhoyouareandwhatyou’lldoandwantnext’basedonwhatyouhavedoneandwantedbefore
-
- Sep 2017
-
Local file Local file
-
In each case data was framed as repressive of notions of civil society or enforcing an impoverished or constrictive notion of citizenship. The perspectives of Tufekci and Cheney-Lippold provide valuable insight into how algorithms and data are powerful shapers of modern life. Yet, they leave little room for a different form of algorithmic citizenship that might emerge where indi-viduals desire to reform technology and data-driven processes. As Couldry and Powell (2014) note, models of algorithmic power (Beer, 2009; Lash, 2007) tend to downplay questions of individual agency. They suggest a need to “highlight not just the risks of creating and sharing data, but the opportunities as well” (p. 5). We should be attentive to moments where meaningful change can occur, even if those changes are fraught with forces of neoliberalism and tinged with technocracy.
-
- Jan 2016