I tried to run ENMTools on Windows. And I set ENMTools option and Maxent option as manual suggested. The “RAM to assign to Maxent” option was set as 512. But when I calculate the niche overlap from two ASCII files, the software kept saying "out of memory". I wonder what could I do now? Any help would be appreciated.
The “RAM to assign to Maxent” option was set as 512 and 1000, but they all failed.
Related
I'm using Splunk to monitor my applications.
I also store resource statistics in my Splunk too.
Goal: I want to find the optimum CPU limit for each container.
How to I write a query that finds an optimum CPU limit? Or the other question is Should I?
Concern1: When I start customizing my query and let's say that I have used MAX(CPU) command. It doesn't mean that my container will be running at level most of the time. So, I might set an unnecessary high limit for my containers.
Let me explain, when I find a CPU limit value via MAX(CPU) command as 10, this top value might be happened because of a bulk operation. So, my container's expected resource may be around 1.2 all the time, except this single 1 operation that one. So, using MAX value won't work.
Concern2: Let's say that I have used the value of AVG(CPU) value and used it. And that is 2, So how many of my operations will be waited for how many minutes after this change? Or how many of them are going to be timed out? It may create a lot of side-effects. How will I decide the real average value? What parameters should be used?
Is it possible to include such conditions in the query? Or do I need an AI to decide it? :)
Here are my givin parameters:
path=statistics.cpus_system_time_secs
path=statistics.cpus_user_time_secs
path=statistics.cpus_nr_periods
path=statistics.cpus_nr_throttled
path=statistics.cpus_throttled_time_secs
path=statistics.cpus_limit
I bet you can ask better questions than me. Let's discuss.
"Optimum" is going to depend greatly on your own environment (resources available, application priority, etc)
You probably want to look at a combination of the following factors:
avg(CPU)
max(CPU) (and time spent there)
min(CPU) (and time spent there)
I suspect your "optimum" limit is going to be a % below your max...but only if you're spending 'a lot' of time maxxed-out
And, of course, being "maxed" may not matter, if other containers are running acceptably
Keep in mind, once you set that limit, your max will drop (as, likely, will your avg)
I have a Redis Master and 2 slaves. All 3 are currently on the same unix server. The memory used by the 3 instances is approximately 3.5 G , 3 G , 3G. There are about 275000 keys in the redis db. About 4000 are hashes. 1 Set has 100000 values. 1 List has 275000 keys in it. Its a List of Hashes and Sets. The server has total memory of 16 GB. Currently 9.5 GB is used. The persistence is currently off. The rdb file is written once in a day by forced background save. Please provide any suggestions for optimizations. max-ziplist configuration is default currently.
Optimizing Hashes
First, let's look at the hashes. Two important questions - how many elements in each hash, and what is the largest value in those hashes? A hash uses the memory efficient ziplist representation if the following condition is met:
len(hash) < hash-max-ziplist-entries && length-of-largest-field(hash) < hash-max-ziplist-value
You should increase the two settings in redis.conf based on your data, but don't increase it more than 3-4 times the default.
Optimizing Sets
A set with 100000 cannot be optimized, unless you provide additional details on your use case. Some general strategies though -
Maybe use HyperLogLog - Are you using the set to count unique elements? If the only commands you run are sadd and scard - maybe you should switch to a hyperloglog.
Maybe use Bloom Filter - Are you using the set to check for existence of a member? If the only commands you run are sadd and sismember - maybe you should implement a bloom filter and use it instead of the set.
How big is each element? - Set members should be small. If you are storing big objects, you are perhaps doing something incorrect.
Optimizing Lists
A single list with 275000 seems wrong. It is going to be slow to access elements in the center of the list. Are you sure you list is the right data structure for your use case?
Change list-compress-depth to 1 or higher. Read about this setting in redis.conf - there are tradeoffs. But for a list of 275000 elements, you certainly want to enable compression.
Tools
Use the open source redis-rdb-tools to analyze your data set (disclaimer: I am the author of this tool). It will tell you how much memory each key is taking. It will help you to decide where to concentrate your efforts on.
You can also refer to this memory optimization cheat sheet.
What else?
You have provided very little details on your use case. The best savings come from picking the right data structure for your use case. I'd encourage you to update your question with more details on what you are storing within the hash / list / set.
We did following configuration and that helped to reduce the memory footprint by 40%
list-max-ziplist-entries 2048
list-max-ziplist-value 10000
list-compress-depth 1
set-max-intset-entries 2048
hash-max-ziplist-entries 2048
hash-max-ziplist-value 10000
Also, we increased the RAM on the linux server and that helped us with the Redis memory issues.
I am trying to build a Sieve of Eratosthenes in Lua and i tried several things but i see myself confronted with the following problem:
The tables of Lua are to small for this scenario. If I just want to create a table with all numbers (see example below), the table is too "small" even with only 1/8 (...) of the number (the number is pretty big I admit)...
max = 600851475143
numbers = {}
for i=1, max do
table.insert(numbers, i)
end
If I execute this script on my Windows machine there is an error message saying: C:\Program Files (x86)\Lua\5.1\lua.exe: not enough memory. With Lua 5.3 running on my Linux machine I tried that too, error was just killed. So it is pretty obvious that lua can´t handle the amount of entries.
I don´t really know whether it is just impossible to store that amount of entries in a lua table or there is a simple solution for this (tried it by using a long string aswell...)? And what exactly is the largest amount of entries in a Lua table?
Update: And would it be possible to manually allocate somehow more memory for the table?
Update 2 (Solution for second question): The second question is an easy one, I just tested it by running every number until the program breaks: 33.554.432 (2^25) entries fit in one one-dimensional table on my 12 GB RAM system. Why 2^25? Because 64 Bit per number * 2^25 = 2147483648 Bits which are exactly 2 GB. This seems to be the standard memory allocation size for the Lua for Windows 32 Bit compiler.
P.S. You may have noticed that this number is from the Euler Project Problem 3. Yes I am trying to accomplish that. Please don´t give specific hints (..). Thank you :)
The Sieve of Eratosthenes only requires one bit per number, representing whether the number has been marked non-prime or not.
One way to reduce memory usage would be to use bitwise math to represent multiple bits in each table entry. Current Lua implementations have intrinsic support for bitwise-or, -and etc. Depending on the underlying implementation, you should be able to represent 32 or 64 bits (number flags) per table entry.
Another option would be to use one or more very long strings instead of a table. You only need a linear array, which is really what a string is. Just have a long string with "t" or "f", or "0" or "1", at every position.
Caveat: String manipulation in Lua always involves duplication, which rapidly turns into n² or worse complexity in terms of performance. You wouldn't want one continuous string for the whole massive sequence, but you could probably break it up into blocks of a thousand, or of some power of 2. That would reduce your memory usage to 1 byte per number while minimizing the overhead.
Edit: After noticing a point made elsewhere, I realized your maximum number is so large that, even with a bit per number, your memory requirements would optimally be about 73 gigabytes, which is extremely impractical. I would recommend following the advice Piglet gave in their answer, to look at Jon Sorenson's version of the sieve, which works on segments of the space instead of the whole thing.
I'll leave my suggestion, as it still might be useful for Sorenson's sieve, but yeah, you have a bigger problem than you realize.
Lua uses double precision floats to represent numbers. That's 64bits per number.
600851475143 numbers result in almost 4.5 Terabytes of memory.
So it's not Lua's or its tables' fault. The error message even says
not enough memory
You just don't have enough RAM to allocate that much.
If you would have read the linked Wikipedia article carefully you would have found the following section:
As Sorenson notes, the problem with the sieve of Eratosthenes is not
the number of operations it performs but rather its memory
requirements.[8] For large n, the range of primes may not fit in
memory; worse, even for moderate n, its cache use is highly
suboptimal. The algorithm walks through the entire array A, exhibiting
almost no locality of reference.
A solution to these problems is offered by segmented sieves, where
only portions of the range are sieved at a time.[9] These have been
known since the 1970s, and work as follows
...
I'm just testing influxdb 1.3.5 for storing a small number (~30-300) of very long integer series (worst case: (86400)*(12*365) [sec/day * ((days/year)*12) * 1 device] = 378.432.000)
e.g. the number of total points would be for 320 devices: (86400)*(12*365)*320 [sec/day * ((days/year)*12) * 320 devices] = 121.098.240.000)
The series cardinality is low, it equals the number of devices. I'm using second-precision timestamps (that mode is enabled when I commit to influxdb via the php-API.
Yes, I really need to keep all the samples, so downsampling is not an option.
I'm inserting the samples as point-arrays of size 86400 per request sorted from oldest to newest. The behaviour is similar (OOM in both cases) for inmem and tsi1 indexing modes.
Despite all that, I'm not able to insert this number of points to the database without crashing it due to out of memory. The host-vm has 8GiB of RAM and 4GiB of Swap which fill up completely. I cannot find anything about that setup being problematic in the documentation. I cannot find a notice that indicates this setup should result in a high RAM usage at all...
Does anyone have a hint on what could be wrong here?
Thanks and all the best!
b-
[ I asked the same question here but received no replies, that's the reason for crossposting: https://community.influxdata.com/t/ever-increasing-ram-usage-with-low-series-cardinality/2555 ]
I found out what the issue most likely was:
I had a bug in my feeder that caused timestamps not being updated to lots of points with distinct values were written over and over again to the same timestamp/tag combination.
If you experience something similar, try double-checking each step in the pipeline for a time concerning error.
This was not the issue unfortunately, the ram usage rises nevertheless then importing more points than before.
I need to use maxima software to deal with data. I try to read data from a text file constructed as
1 2 3
11 22 33
ect.
Following comands allow for loading data sufficiently.
load(numericalio);
read_matrix("path to the file");
The problem arises when I apply them to a more realistic (larger) data set. In this case the message appears Expression longer than allowed by the configuration setting.
How to overcome this problem? I cannot see any option in configuration menu. I would be grateful for advice.
I ran into the same error message today, at it seems to be related to the size of the output that wxMaxima receives from the Maxima executable.
If you wish to display the output regardless, you can change it in the configuration here:
Edit>Configure>Worksheet>Show long expressions
Note that showing a massive expression or amount of data may dramatically slow the program down, so consider hiding the output (use a $ instead of a ; at the end of your lines) if you don't need to visualize the data.