NVIDIA specifies that their GeForce RTX 3090 (Ti) has 24 GB of memory. How are you supposed to know how much data you can fit on it, when some sources use 1 GB = 1,0243 bytes, while other sources use 1 GB = 1,0003 bytes? Can you assume that hardware manufacturers always use 1,000-base since that means they can write higher number in the specifications, or do some hardware manufacturers still use 1,024-base?
I would expect 1 GB = 1,024^3 as I only encountered 1 GB = 1,000^3 in the context of storage as in SSD.
When I do docker stats I see that usage is greater than 100% most of the times. I have a machine which has 8 cores. So, does below output mean that 100% CPU means one core is totally occupied. So, 690% means close to 7 cores is totally occupied ?
d99e067cfffc 690.00% 5.517 GiB / 12.7 GiB 43.46% 1.47 GB / 1.03 GB 9.15 MB / 0 B 338
Exactly as you stated. You can have up to N * 100% CPU usage when N is the number of cores you have.
By the way, you can run the container with a --cpus <your_num> flag to limit the usage of CPU cores if you like.
More details in the official docs.
i executed a job in Google Cloud Dataflow and now i'm seeing the result on StackDriver. I don't understand the memory chart. I used only 1 and after 3 worker but the scale of this chart is the order of TB to second. it is normal? or maybe the scale is GB? in the metrics of this job, also, in a precise instant that i saw, the value of actual memory was 45 GB, and it isn't in this chart and is much smaller. can someone explain me this chart?
The Total memory usage time is one of the Dataflow metrics used to measure consumption of computing capacity (system memory in this case). This is
The total GB seconds of memory allocated to this Dataflow job.
Customers are billed for the consumed resources accordingly with the established Pricing .
Memory consumption is measured in GB-seconds. 1 GB.s is 1 second of wall clock time with 1GB of memory provisioned. Compute time is measured in 100ms increments, rounded up to the nearest increment.
Since memory usage on the chart is a time-aggregated value, values expressed in TB.s can be converted into GB.h by dividing by 3600 s:
1 GB.h = 3.6 TB.s
The curve shape and Y-coordinate depend on the aggregation and alignment settings you use: max or mean, 1m or 1h alignment period, etc. For instance in case of a short peak load, the wide time window will act as a big denominator for the mean aligner.
Memory usage (measured in GB or TB) and memory usage time (typically measured in GB hr or TB s) are different measurements.
The Dataflow UI gives the following explanation for memory time: "The total running time for all memory used by all workers associated with your job. For example, if your job used 3GB of memory for 4 hours, the total memory time is 12 [GB] hours."
I'm reading this CPU specification: http://ark.intel.com/products/67356/Intel-Core-i7-3612QM-Processor-6M-Cache-up-to-3_10-GHz-rPGA
It says the CPU has 2 channels. So I think it has 2 memory controller inside. Then the max memory bandwidth should be 1.6GHz * 64bits * 2 * 2 = 51.2 GB/s if the supported DDR3 RAM are 1600MHz. But the specification says its max memory bandwidth is 25.6 GB/s.
I multiplied two 2s here, one for the Double Data Rate, another for the memory channel.
Is it the problem of the specification? or I have some miss understanding?
Double data rate memory specs usually already take into account that its effective frequency is doubled. "1600 MHz memory" really runs on 800 Mhz, so you can leave out one factor of 2 from your calculation.
IDE,SCSI,SSD,SATA or all of those.
I'm surprised: Figure 3 in the middle of this article, The Pathologies of Big Data, says that memory is only about 6 times faster when you're doing sequential access (350 Mvalues/sec for memory compared with 58 Mvalues/sec for disk); but it's about 100,000 times faster when you're doing random access.
Random Access Memory (RAM) takes nanoseconds to read from or write to, while hard drive (IDE, SCSI, SATA that I'm aware of) access speed is measured in milliseconds.
2016 Hardware Update: Actual read/write seq throughput
Now the Samsung 940 PRO SSD
reading at 3,500 MB/sec
writing at 2,100 MB/sec
Ram got faster too
reading at 61,000 MB/sec
writing at 48,000 MB/sec..
So now using this metric, RAM looks to be 20x faster than the stuff around when #ChrisW wrote his answer, not 100,000. And, SSDs are 10 times faster than RAM was when he wrote this question.
An important consideration is that we're only measuring memory bandwidth not latency.
It's not precisely about SCSI drives, but I think that the Latency Numbers Every Programmer Should Know table could assist you in understanding the speed and the difference between different latency numbers, including storage options.
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
Here is a great visual representation that will help you to better understand the scale:
https://people.eecs.berkeley.edu/~rcs/research/interactive_latency.html
RAM is 100 Thousand Times Faster than Disk for Database Access from
http://www.directionsmag.com/articles/ram-is-100-thousand-times-faster-than-disk-for-database-access/123964
Accessing the RAM is in the order of nanoseconds ( 10e-9 seconds ),
while accessing data on the disk or the network is in the order of
milliseconds (10e-3 seconds).
from Node.JS Design Patterns