| Latency Comparison Numbers (~2012) | |
| ---------------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD | |
| Read 1 MB sequentially from memory 250,000 ns 250 us | |
| Round trip within same datacenter 500,000 ns 500 us | |
| Read 1 MB sequentially from SSD* 1,000,000 ns 1,000 us 1 ms ~1GB/sec SSD, 4X memory | |
| Disk seek 10,000,000 ns 10,000 us 10 ms 20x datacenter roundtrip | |
| Read 1 MB sequentially from disk 20,000,000 ns 20,000 us 20 ms 80x memory, 20X SSD | |
| Send packet CA->Netherlands->CA 150,000,000 ns 150,000 us 150 ms | |
| Notes | |
| ----- | |
| 1 ns = 10^-9 seconds | |
| 1 us = 10^-6 seconds = 1,000 ns | |
| 1 ms = 10^-3 seconds = 1,000 us = 1,000,000 ns | |
| Credit | |
| ------ | |
| By Jeff Dean: http://research.google.com/people/jeff/ | |
| Originally by Peter Norvig: http://norvig.com/21-days.html#answers | |
| Contributions | |
| ------------- | |
| 'Humanized' comparison: https://gist.github.com/hellerbarde/2843375 | |
| Visual comparison chart: http://i.imgur.com/k0t1e.png |
I think given increased use of GPUs / TPUs it might be interesting numbers to add here now. Like: 1MB over PCIexpress to GPU memory, Computing 100 prime numbers per core of CPU compared to CPU, reading 1 MB from GPU memory to GPU etc.
useful information & thanks
Some data of the Berkeley interactive version (https://people.eecs.berkeley.edu/~rcs/research/interactive_latency.html ) is estimated, eg: 4 µs in 2019 to read 1 MB sequentially from memory; it seems too fast.
this is a great idea.
how about the time to complete a DNS request - UDP packet request and response with a DNS server having, say, 1ms response time, with the DNS server being 5ms packet time-of-flight away?
What effect on latency has the use multiple native threads on doing operations possible due to proper mutex locking? Assumed you have:
- an operation 1024 ns operation in 1st level cache
- 2 x lock unlock mutex (50 ns)
- move it from/to main memory (200 ns)
Now, I wonder about malloc latency, can you tell about it? It is definitely missing because I can compute data without any lock as owning the data.
interesting when you see in a glance. but would't it be good to use one unit in the comparison e.g. memory page 4k?
Nanoseconds
It's an excellent explanation. I had to search the video because the account was closed. Here's the result I got: https://www.youtube.com/watch?v=9eyFDBPk4Yw
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
This doesn't look right to me. 1 Gbps = 125, 000 KB/s, the time should be 1 / 125,000 = 8 * 10^-6 seconds which is 8000ns
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
This doesn't look right to me. 1 Gbps = 125, 000 KB/s, the time should be 1 / 125,000 = 8 * 10^-6 seconds which is 8000ns
For a direct host-to-host connection with 1000BaseT interfaces, a wire latency of 8µs is correct.
However, if the hosts are connected using SGMII, the Serial Gigabit Media Independent Interface, data is 8b10b encoded, meaning 10 bits are sent for every 8 bits of data, leading to a latency of 10µs.
Jeff may also have been referring to the fact that in a large cluster you'll have a few switches between the hosts, so even where 1000BaseT is in use, the added switching latency (even for switches operating in cut-through mode) for, say, 2 switches can approach 2µs.
In any event, the main thing to take away from these numbers are the orders of magnitude differences between latency for various methods of I/O.
Fancy unicode version:
Latency Comparison Numbers (~2012)
╭───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│L1 cache reference 0.5 ns │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Branch mispredict 5 ns │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│L2 cache reference 7 ns 14× L1 cache │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Mutex lock/unlock 25 ns │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Main memory reference 100 ns 20× L2 cache, 200× L1 cache │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Compress 1K bytes with Zippy 3,000 ns 3 μs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Send 1K bytes over 1 Gbps network 10,000 ns 10 μs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Read 4K randomly from SSD* 150,000 ns 150 μs ~1GB/sec SSD │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Read 1 MB sequentially from memory 250,000 ns 250 μs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Round trip within same datacenter 500,000 ns 500 μs │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Read 1 MB sequentially from SSD* 1,000,000 ns 1,000 μs 1 ms ~1GB/sec SSD, 4× memory │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Disk seek 10,000,000 ns 10,000 μs 10 ms 20× datacenter roundtrip │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Read 1 MB sequentially from disk 20,000,000 ns 20,000 μs 20 ms 80× memory, 20× SSD │
├───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┤
│Send packet CA→Netherlands→CA 150,000,000 ns 150,000 μs 150 ms │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Are these numbers still relevant in 2020? Or this needs an update?
Are these numbers still relevant in 2020? Or this needs an update?
I think hardwares are so expensive that can't update them~
One thing that is misleading is that different units are used for send over 1Gbps versus read 1 MB from RAM. RAM is at least x20 times faster, but it ranks below send over network which is misleading. They should have used the same 1MB for network and RAM.
need a solar system type visualization for this, so we can really appreciate the change of scale.
Hi
I liked your request and made an comparison. One unit is Mass of earth not radius.
| Operation | Time in Nano Seconds | Astronomical Unit of Weight |
|---|---|---|
| L1 cache reference | 0.5 ns | 1/2 Earth or Five times Mars |
| Branch mispredict | 5 ns | 5 Earths |
| L2 cache reference | 7 ns | 7 Earths |
| Mutex lock/unlock | 25 ns | Roughly [Uranus +Neptune] |
| Main memory reference | 100 ns | Roughly Saturn + 5 Earths |
| Compress 1K bytes with Zippy | 3,000 ns | 10 Jupiters |
| Send 1K bytes over 1 Gbps network | 10,000 ns | 20 Times All the Planets of the Solar System |
| Read 4K randomly from SSD* | 150,000 ns | 1.6 times Red Dwarf Wolf 359 |
| Read 1 MB sequentially from memory | 250,000 ns | Quarter of the Sun |
| Round trip within same datacenter | 500,000 ns | Half of the Mass of Sun |
| Read 1 MB sequentially from SSD* | 1,000,000 ns | Sun |
| Disk seek | 10,000,000 ns | 10 Suns |
| Read 1 MB sequentially from disk | 20,000,000 ns | Red Giant R136a2 |
| Send packet CA->Netherlands->CA | 150,000,000 ns | An Intermediate Sized Black Hole |
https://docs.google.com/spreadsheets/d/13R6JWSUry3-TcCyWPbBhD2PhCeAD4ZSFqDJYS1SxDyc/edit?usp=sharing
need a solar system type visualization for this, so we can really appreciate the change of scale.
Hi
I liked your request and made an comparison. One unit is Mass of earth not radius.
For me the best way of making this "more human relatable" would be to treat nanoseconds as seconds and then convert the large values.
eg. 150,000,000s = ~4.75 years
I've been doing some more work inspired by this, surfacing more numbers, and adding throughput:
Is there a 2021 updated edition?
@sirupsen I love your project and I'm signed up for the newsletter. Currently making Anki flashcards :)
There are some large discrepancies between your numbers and the ones found here (not sure where these numbers came from):
https://colin-scott.github.io/personal_website/research/interactive_latency.html
I'm curious what's causing them. Specifically, 1MB sequential memory read: 100us vs 3us.
@ellingtonjp My program is getting ~100 us, and this one says 250 us (from 2012). Lines up to me with some increases in performance since :) Not sure how you got 3 us
@sirupsen I was referring to the numbers here https://colin-scott.github.io/personal_website/research/interactive_latency.html
The 2020 version of "Read 1,000,000 bytes sequentially from memory" shows 3us. Not sure where that comes from though. Yours seems more realistic to me
Ahh, sorry I read your message too quick. Yeah, unclear to me how someone would get 3us. The code I use for this is very simple. It took reading the x86 a few times to ensure that the compiler didn't optimize it out. I do summing, which is one of the lightest workloads you could do in a loop like that. So I think it's quite realistic. Maybe that person's script it was optimized out?
To everyone interested in numbers like this:
@sirupsen 's project is really good. He gave an excellent talk on the "napkin math" skill and has a newsletter with monthly challenges for practicing putting these numbers to use.
Newsletter: https://sirupsen.com/napkin/
Github: https://github.com/sirupsen/napkin-math
Talk: https://www.youtube.com/watch?v=IxkSlnrRFqc
:)
Light to reach the moon 2,510,000,000 ns 2,510,000 us 2,510 ms 2.51 s
Heh, imagine this transposed into human distances.
1ns = 1 step, or 2 feet.
L1 cache reference = reaching 1 foot across your desk to pick something up
Datacentre roundtrip = 94 mile hike.
Internet roundtrip (California to Netherlands) = Walk around the entire earth. Wait! You're not done. Then walk from London, to Havana. Oh, and then to Jacksonville, Florida. Then you're done.
useful information & thanks
What about register access timings?
Markdown version :p
| Operation | ns | µs | ms | note |
|---|---|---|---|---|
| L1 cache reference | 0.5 ns | |||
| Branch mispredict | 5 ns | |||
| L2 cache reference | 7 ns | 14x L1 cache | ||
| Mutex lock/unlock | 25 ns | |||
| Main memory reference | 100 ns | 20x L2 cache, 200x L1 cache | ||
| Compress 1K bytes with Zippy | 3,000 ns | 3 µs | ||
| Send 1K bytes over 1 Gbps network | 10,000 ns | 10 µs | ||
| Read 4K randomly from SSD* | 150,000 ns | 150 µs | ~1GB/sec SSD | |
| Read 1 MB sequentially from memory | 250,000 ns | 250 µs | ||
| Round trip within same datacenter | 500,000 ns | 500 µs | ||
| Read 1 MB sequentially from SSD* | 1,000,000 ns | 1,000 µs | 1 ms | ~1GB/sec SSD, 4X memory |
| Disk seek | 10,000,000 ns | 10,000 µs | 10 ms | 20x datacenter roundtrip |
| Read 1 MB sequentially from disk | 20,000,000 ns | 20,000 µs | 20 ms | 80x memory, 20X SSD |
| Send packet CA -> Netherlands -> CA | 150,000,000 ns | 150,000 µs | 150 ms |
Is there any resources when one can test himself with a tasks involving these numbers?
E.g. calculate how much time will it take to read 5Mb from DB in another datacenter and get it back?
That would be a great test of applying those numbers in some real use cases.