The SBCL created stumpwm is greater than 40 MB, which is too big just for a Window Manager. The C produced DWM is about 30K.
We donot need a complete CL environment, How can i make stumpwm smaller ?
Sincerely !
SBCL supports compressed core images since 1.0.52. See http://xach.livejournal.com/295584.html for details.
Related
I would like to increase (or decrease) the amount of memory available to R. What are the methods for achieving this?
From:
http://gking.harvard.edu/zelig/docs/How_do_I2.html (mirror)
Windows users may get the error that R
has run out of memory.
If you have R already installed and
subsequently install more RAM, you may
have to reinstall R in order to take
advantage of the additional capacity.
You may also set the amount of
available memory manually. Close R,
then right-click on your R program
icon (the icon on your desktop or in
your programs directory). Select
``Properties'', and then select the
``Shortcut'' tab. Look for the
``Target'' field and after the closing
quotes around the location of the R
executible, add
--max-mem-size=500M
as shown in the figure below. You may
increase this value up to 2GB or the
maximum amount of physical RAM you
have installed.
If you get the error that R cannot
allocate a vector of length x, close
out of R and add the following line to
the ``Target'' field:
--max-vsize=500M
or as appropriate. You can always
check to see how much memory R has
available by typing at the R prompt
memory.limit()
which gives you the amount of available memory in MB. In previous versions of R you needed to use: round(memory.limit()/2^20, 2).
Use memory.limit(). You can increase the default using this command, memory.limit(size=2500), where the size is in MB. You need to be using 64-bit in order to take real advantage of this.
One other suggestion is to use memory efficient objects wherever possible: for instance, use a matrix instead of a data.frame.
For linux/unix, I can suggest unix package.
To increase the memory limit in linux:
install.packages("unix")
library(unix)
rlimit_as(1e12) #increases to ~12GB
You can also check the memory with this:
rlimit_all()
for detailed information:
https://rdrr.io/cran/unix/man/rlimit.html
also you can find further info here:
limiting memory usage in R under linux
Microsoft Windows accepts any memory request from processes if it could be done.
There is no limit for the memory that can be provided to a process, except the Virtual Memory Size.
Virtual Memory Size is 4GB in 32bit systems for any processes, no matter how many applications you are running. Any processes can allocate up to 4GB memory in 32bit systems.
In practice, Windows automatically allocates some parts of allocated memory from RAM or page-file depending on processes requests and paging file mechanism.
But another limit is the size of paging file. If you have a small paging-file, you cannot allocated large memories. You could increase the size of paging file according to Microsoft to have more memory space.
Buy more ram
Switch to a 64-bit OS. Combine with point 1.
To increase the amount of memory allocated to R you can use memory.limit
memory.limit(size = ...)
Or
memory.size(max = ...)
About the arguments
size - numeric. If NA report the memory limit, otherwise request a new limit, in Mb. Only values of up to 4095 are allowed on 32-bit R builds, but see ‘Details’.
max - logical. If TRUE the maximum amount of memory obtained from the OS is reported, if FALSE the amount currently in use, if NA the memory limit.
In RStudio, to increase:
file.edit(file.path("~", ".Rprofile"))
then in .Rprofile type this and save
invisible(utils::memory.limit(size = 60000))
To decrease:
open .Rprofile
invisible(utils::memory.limit(size = 30000))
save and restart RStudio.
I read couple of questions on SO for this topic(SIMD Mode), but still slight clarification/confirmation of how things work is required.
Why use SIMD if we have GPGPU?
SIMD intrinsics - are they usable on gpus?
CPU SIMD vs GPU SIMD?
Are following points correct,if I compile the code in SIMD-8 mode ?
1) it means 8 instructions of different work items are getting executing in parallel.
2) Does it mean All work items are executing the same instruction only?
3) if each wrok item code contains vload16 load then float16 operations and then vstore16 operations only. SIMD-8 mode will still work. I mean to say is it true GPU is till executing the same instruction (either vload16/ float16 / vstore16) for all 8 work items?
How should I understand this concept?
In the past many OpenCL vendors required to use vector types to be able to use SIMD. Nowadays OpenCL vendors are packing work items into SIMD so there is no need to use vector types. Whether is preffered to use vector types can be checked by querying for: CL_DEVICE_PREFERRED_VECTOR_WIDTH_<CHAR, SHORT, INT, LONG, FLOAT, DOUBLE>.
On Intel if vector type is used the vectorizer first scalarize them and then re-vectorize to make use of the wide instruction set. This is probably going to be similar on the other platforms.
I made a small test benchmark comparing .NET's System.Security.Cryptography AES implementation vs BouncyCastle.Org's AES.
Link to GitHub code: https://github.com/sidshetye/BouncyBench
I'm particularly interested in AES-GCM since it's a 'better' crypto algorithm and .NET is missing it. What I noticed was that while the AES implementations are very comparable between .NET an BouncyCastle, the GCM performance is quite poor (see extra background below for more). I suspect it's due to many buffer copies or something. To look deeper, I tried profiling the code (VS2012 => Analyze menu bar option => Launch performance wizard) and noticed that there was a LOT of CPU burn inside mscorlib.dll
Question: How can I figure out what's eating most of the CPU in such a case? Right now all I know is "some lines/calls in Init() burn 47% of CPU inside mscorlib.ni.dll" - but without knowing what specific lines, I don't know where to (try and) optimize. Any clues?
Extra background:
Based on the "The Galois/Counter Mode of Operation (GCM)" paper by David A. McGrew, I read "Multiplication in a binary field can use a variety of time-memory tradeoffs. It can be implemented with no key-dependent memory, in which case it will generally run several times slower than AES. Implementations that are willing to sacrifice modest amounts of memory can easily realize speeds greater than that of AES."
If you look at the results, the basic AES-CBC engine performances are very comparable. AES-GCM adds the GCM and reuses the AES engine beneath it in CTR mode (faster than CBC). However, GCM also adds multiplication in the GF(2^128) field in addition to the CTR mode, so there could be other areas of slowdown. Anyway, that's why I tried profiling the code.
For the interested, where is my quick test performance benchmark. It's inside a Windows 8 VM and YMMV. The test is configurable but currently it's to simulate crypto overhead in encrypting many cells of a database (=> many but small plaintext input)
Creating initial random bytes ...
Benchmark test is : Encrypt=>Decrypt 10 bytes 100 times
Name time (ms) plain(bytes) encypted(bytes) byte overhead
.NET ciphers
AES128 1.5969 10 32 220 %
AES256 1.4131 10 32 220 %
AES128-HMACSHA256 2.5834 10 64 540 %
AES256-HMACSHA256 2.6029 10 64 540 %
BouncyCastle Ciphers
AES128/CBC 1.3691 10 32 220 %
AES256/CBC 1.5798 10 32 220 %
AES128-GCM 26.5225 10 42 320 %
AES256-GCM 26.3741 10 42 320 %
R - Rerun tests
C - Change size(10) and iterations(100)
Q - Quit
This is a rather lame move from Microsoft as they obviously broke a feature that worked well before Windows 8, but no longer, as explained in this MSDN blog post:
:
On Windows 8 the profiler uses a different underlying technology than
what it does on previous versions of Windows, which is why the
behavior is different on Windows 8. With the new technology, the
profiler needs the symbol file (PDB) to know what function is
currently executing inside NGEN’d images.
(...)
It is however on our backlog to implement in the next version of Visual Studio.
The post gives directions to generate the PDB files yourself (thanks!).
since I'm a newbie to Common Lisp I tried to solve problems on SPOJ by using Common Lisp (SBCL). The first problem is a simple task of reading numbers until number 42 is found. Here's my solution:
(defun study-num ()
(let ((num (parse-integer (read-line t))))
(when (not (= num 42))
(format t "~A~%" num)
(study-num))))
(study-num)
The solution is accepted. But when I looked into the details of the result I found it used 57M of MEM! It's bloody unreasonable but I can't figure out why. What can I do to make an optimization?
You are making repeated recursive calls, without enough optimization switched on to enable tail-call elimination (SBCL does do this, but only when you have "optimize for speed" set high and "optimize for debug info" set low).
The Common Lisp standard leaves tail-call elimination as an implementation quality issue and provides other looping constructs (like LOOP or DO, both possibly suitable for this application).
In addition, a freshly started SBCL is probably going to be larger than you expect, due to needing to pull in its runtime environment and base image.
I think yo are not realizing that Common Lisp is an online language environment with full library and compiler loaded into RAM just to give you the first prompt. After that, load in your program is probably a hardly even noticeable increase in size. Lisp does not compile and link an independent executable file made of only your code and whatever lib routines reachable from your code. That's what C and similar languages do. Instead, Lisp adds your code into its already sizeable online environment. As a new user it seams horrible. But if you have a modern general purpose computer with 100's MB of RAM, it quickly becomes something you can forget about as you enjoy the benefits of the online environment. Thins is also called a "dynamic language environment."
Various Lisp implementations have different ways to create programs. One is to dump an image of the memory of a Lisp system and to write that to disk. On restart this image is loaded with a runtime and then started again. This is quite common.
This is also what SBCL does when it saves an executable. Thus this executable includes the full SBCL.
Some other implementations are creating smaller executables using images (CLISP), some can remove unused code from executables (Allegro CL, LispWorks) and others are creating very small programs via compilation to C (mocl).
SBCL has only one easy way to reduce the size of an executable: one can compress the image.
I would like to increase (or decrease) the amount of memory available to R. What are the methods for achieving this?
From:
http://gking.harvard.edu/zelig/docs/How_do_I2.html (mirror)
Windows users may get the error that R
has run out of memory.
If you have R already installed and
subsequently install more RAM, you may
have to reinstall R in order to take
advantage of the additional capacity.
You may also set the amount of
available memory manually. Close R,
then right-click on your R program
icon (the icon on your desktop or in
your programs directory). Select
``Properties'', and then select the
``Shortcut'' tab. Look for the
``Target'' field and after the closing
quotes around the location of the R
executible, add
--max-mem-size=500M
as shown in the figure below. You may
increase this value up to 2GB or the
maximum amount of physical RAM you
have installed.
If you get the error that R cannot
allocate a vector of length x, close
out of R and add the following line to
the ``Target'' field:
--max-vsize=500M
or as appropriate. You can always
check to see how much memory R has
available by typing at the R prompt
memory.limit()
which gives you the amount of available memory in MB. In previous versions of R you needed to use: round(memory.limit()/2^20, 2).
Use memory.limit(). You can increase the default using this command, memory.limit(size=2500), where the size is in MB. You need to be using 64-bit in order to take real advantage of this.
One other suggestion is to use memory efficient objects wherever possible: for instance, use a matrix instead of a data.frame.
For linux/unix, I can suggest unix package.
To increase the memory limit in linux:
install.packages("unix")
library(unix)
rlimit_as(1e12) #increases to ~12GB
You can also check the memory with this:
rlimit_all()
for detailed information:
https://rdrr.io/cran/unix/man/rlimit.html
also you can find further info here:
limiting memory usage in R under linux
Microsoft Windows accepts any memory request from processes if it could be done.
There is no limit for the memory that can be provided to a process, except the Virtual Memory Size.
Virtual Memory Size is 4GB in 32bit systems for any processes, no matter how many applications you are running. Any processes can allocate up to 4GB memory in 32bit systems.
In practice, Windows automatically allocates some parts of allocated memory from RAM or page-file depending on processes requests and paging file mechanism.
But another limit is the size of paging file. If you have a small paging-file, you cannot allocated large memories. You could increase the size of paging file according to Microsoft to have more memory space.
Buy more ram
Switch to a 64-bit OS. Combine with point 1.
To increase the amount of memory allocated to R you can use memory.limit
memory.limit(size = ...)
Or
memory.size(max = ...)
About the arguments
size - numeric. If NA report the memory limit, otherwise request a new limit, in Mb. Only values of up to 4095 are allowed on 32-bit R builds, but see ‘Details’.
max - logical. If TRUE the maximum amount of memory obtained from the OS is reported, if FALSE the amount currently in use, if NA the memory limit.
In RStudio, to increase:
file.edit(file.path("~", ".Rprofile"))
then in .Rprofile type this and save
invisible(utils::memory.limit(size = 60000))
To decrease:
open .Rprofile
invisible(utils::memory.limit(size = 30000))
save and restart RStudio.