I've found this interesting and powerful tool called IACA (the Intel Architecture Code Analyzer), but I have trouble understanding it. What can I do with it, what are its limitations and how can I:
Use it to analyze code in C or C++?
Use it to analyze code in x86 assembler?
2019-04: Reached EOL. Suggested alternative: LLVM-MCA
2017-11: Version 3.0 released (latest as of 2019-05-18)
2017-03: Version 2.3 released
What it is:
IACA (the Intel Architecture Code Analyzer) is a (2019: end-of-life) freeware, closed-source static analysis tool made by Intel to statically analyze the scheduling of instructions when executed by modern Intel processors. This allows it to compute, for a given snippet,
In Throughput mode, the maximum throughput (the snippet is assumed to be the body of an innermost loop)
In Latency mode, the minimum latency from the first instruction to the last.
In Trace mode, prints the progress of instructions through their pipeline stages.
when assuming optimal execution conditions (All memory accesses hit L1 cache and there are no page faults).
IACA supports computing schedulings for Nehalem, Westmere, Sandy Bridge, Ivy Bridge, Haswell, Broadwell and Skylake processors as of version 2.3 and Haswell, Broadwell and Skylake as of version 3.0.
IACA is a command-line tool that produces ASCII text reports and Graphviz diagrams. Versions 2.1 and below supported 32- and 64-bit Linux, Mac OS X and Windows and analysis of 32-bit and 64-bit code; Version 2.2 and up only support 64-bit OSes and analysis of 64-bit code.
How to use it:
IACA's input is a compiled binary of your code, into which have been injected two markers: a start marker and an end marker. The markers make the code unrunnable, but allow the tool to find quickly the relevant pieces of code and analyze them.
You do not need the ability to run the binary on your system; In fact, the binary supplied to IACA can't run anyways because of the presence of the injected markers in the code. IACA only requires the ability to read the binary to be analyzed. Thus it is possible, using IACA, to analyze a Haswell binary employing FMA instructions on a Pentium III machine.
C/C++
In C and C++, one gains access to marker-injecting macros with #include "iacaMarks.h", where iacaMarks.h is a header that ships with the tool in the include/ subdirectory.
One then inserts the markers around the innermost loop of interest, or the straight-line chunk of interest, as follows:
/* C or C++ usage of IACA */
while(cond){
IACA_START
/* Loop body */
/* ... */
}
IACA_END
The application is then rebuilt as it otherwise would with optimizations enabled (In Release mode for users of IDEs such as Visual Studio). The output is a binary that is identical in all respects to the Release build except with the presence of the marks, which make the application non-runnable.
IACA relies on the compiler not reordering the marks excessively; As such, for such analysis builds certain powerful optimizations may need to be disabled if they reorder the marks to include extraneous code not within the innermost loop, or exclude code within it.
Assembly (x86)
IACA's markers are magic byte patterns injected at the correct location within the code. When using iacaMarks.h in C or C++, the compiler handles inserting the magic bytes specified by the header at the correct location. In assembly, however, you must manually insert these marks. Thus, one must do the following:
; NASM usage of IACA
mov ebx, 111 ; Start marker bytes
db 0x64, 0x67, 0x90 ; Start marker bytes
.innermostlooplabel:
; Loop body
; ...
jne .innermostlooplabel ; Conditional branch backwards to top of loop
mov ebx, 222 ; End marker bytes
db 0x64, 0x67, 0x90 ; End marker bytes
It is critical for C/C++ programmers that the compiler achieve this same pattern.
What it outputs:
As an example, let us analyze the following assembler example on the Haswell architecture:
.L2:
vmovaps ymm1, [rdi+rax] ;L2
vfmadd231ps ymm1, ymm2, [rsi+rax] ;L2
vmovaps [rdx+rax], ymm1 ; S1
add rax, 32 ; ADD
jne .L2 ; JMP
We add immediately before the .L2 label the start marker and immediately after jne the end marker. We then rebuild the software, and invoke IACA thus (On Linux, assumes the bin/ directory to be in the path, and foo to be an ELF64 object containing the IACA marks):
iaca.sh -64 -arch HSW -graph insndeps.dot foo
, thus producing an analysis report of the 64-bit binary foo when run on a Haswell processor, and a graph of the instruction dependencies viewable with Graphviz.
The report is printed to standard output (though it may be directed to a file with a -o switch). The report given for the above snippet is:
Intel(R) Architecture Code Analyzer Version - 2.1
Analyzed File - ../../../tests_fma
Binary Format - 64Bit
Architecture - HSW
Analysis Type - Throughput
Throughput Analysis Report
--------------------------
Block Throughput: 1.55 Cycles Throughput Bottleneck: FrontEnd, PORT2_AGU, PORT3_AGU
Port Binding In Cycles Per Iteration:
---------------------------------------------------------------------------------------
| Port | 0 - DV | 1 | 2 - D | 3 - D | 4 | 5 | 6 | 7 |
---------------------------------------------------------------------------------------
| Cycles | 0.5 0.0 | 0.5 | 1.5 1.0 | 1.5 1.0 | 1.0 | 0.0 | 1.0 | 0.0 |
---------------------------------------------------------------------------------------
N - port number or number of cycles resource conflict caused delay, DV - Divider pipe (on port 0)
D - Data fetch pipe (on ports 2 and 3), CP - on a critical path
F - Macro Fusion with the previous instruction occurred
* - instruction micro-ops not bound to a port
^ - Micro Fusion happened
# - ESP Tracking sync uop was issued
# - SSE instruction followed an AVX256 instruction, dozens of cycles penalty is expected
! - instruction not supported, was not accounted in Analysis
| Num Of | Ports pressure in cycles | |
| Uops | 0 - DV | 1 | 2 - D | 3 - D | 4 | 5 | 6 | 7 | |
---------------------------------------------------------------------------------
| 1 | | | 1.0 1.0 | | | | | | CP | vmovaps ymm1, ymmword ptr [rdi+rax*1]
| 2 | 0.5 | 0.5 | | 1.0 1.0 | | | | | CP | vfmadd231ps ymm1, ymm2, ymmword ptr [rsi+rax*1]
| 2 | | | 0.5 | 0.5 | 1.0 | | | | CP | vmovaps ymmword ptr [rdx+rax*1], ymm1
| 1 | | | | | | | 1.0 | | | add rax, 0x20
| 0F | | | | | | | | | | jnz 0xffffffffffffffec
Total Num Of Uops: 6
The tool helpfully points out that currently, the bottleneck is the Haswell frontend and Port 2 and 3's AGU. This example allows us to diagnose the problem as the store not being processed by Port 7, and take remedial action.
Limitations:
IACA does not support a certain few instructions, which are ignored in the analysis. It does not support processors older than Nehalem and does not support non-innermost loops in throughput mode (having no ability to guess which branch is taken how often and in what pattern).
Related
Using zlib in a program and noticed a one bit difference in how "foo" is compressed on Windows 1F8B080000000000000A4BCBCF07002165738C03000000 and Linux 1F8B08000000000000034BCBCF07002165738C03000000. Both decompress back to "foo"
I decided to check outside our code to see if the implementation was correct and used the test programs in the zlib repository to double check. I got the same results:
Linux: echo -n foo| ./minigzip64 > text.txt'
Windows: echo|set /p="foo" | minigzip > text.txt
What would account for this difference? Is it a problem?
1F8B 0800 0000 0000 000 *3/A* 4BCB CF07 0021 6573 8C03 0000 00
First off, if it decompresses to what was compressed, then it's not a problem. Different compressors, or the same compressor at different settings, or even the same compressor with the same settings, but different versions, can produce different compressed output from the same input.
Second, the compressed data in this case is identical. Only the last byte
of the gzip header that precedes the compressed data is different. That byte identifies the originating operating system. Hence it rightly varies between Linux and Windows.
Even on the same operating system, the header can vary since it carries a modification date and time. However in both your cases the modification date and time was left out (set to zeros).
Just to add to the accepted answer here. I got curious and tried out for myself, saving the raw data and opening with 7zip:
Windows:
Linux:
You can immediately notice that the only field that's different is the Host OS.
What the data means
Header Data Footer
1F8B080000000000000A | 4BCBCF0700 | 2165738C03000000
Let's break that down.
Header
First, from this answer I realize it's actually a gzip instead of a zlib header:
Level ZLIB GZIP
1 | 78 01 | 1F 8B
9 | 78 DA | 1F 8B
Further searching led me to an article about Gzip on forensics wiki.
The values in this case are:
Offset Size Value Description
0 | 2 | 1f8b | Signature (or identification byte 1 and 2)
2 | 1 | 08 | Compression Method (deflate)
3 | 1 | | Flags
4 | 4 | | Last modification time
8 | 1 | | Compression flags (or extra flags)
9 | 1 | 0A | Operating system (TOPS-20)
Footer
Offset Size Value Description
0 | 4 | 2165738C | Checksum (CRC-32) (Little endian)
4 | 4 | 03 | Uncompressed data size Value in bytes.
Interesting thing to note here is that even if the Last modification time
and Operating system in header is different, it will compress to the same data
with the same checksum in the footer.
The IETF RFC has a more detailed summary of the format
I'm struggling to find a reliable way to get the number of hardware threads on Windows. I am running a Windows 7 Professional SP1 64-bit on a machine with dual CPU Intel Xeon E5-2699 v3 # 2.30GHz totalizing 36 cores and 72 threads.
I have tried different methods to get the number of cores, and I have found that only two of them seem to work accurately in a 32-bit or 64-bit process. Here are my results:
+------------------------------------------------+----------------+----------------+
| Methods | 32-bit process | 64-bit process |
+------------------------------------------------+----------------+----------------+
| GetSystemInfo->dwNumberOfProcessors | 32 | 36 |
| GetNativeSystemInfo->dwNumberOfProcessors | 36 | 36 |
| GetLogicalProcessorInformation | 36 | 36 |
| GetProcessAffinityMask.processAffinityMask | 32 | 32 |
| GetProcessAffinityMask.systemAffinityMask | 32 | 32 |
| omp_get_num_procs | 32 | 36 |
| getenv("NUMBER_OF_PROCESSORS") | 36 | 36 |
| GetActiveProcessorCount(ALL_PROCESSOR_GROUPS) | 64 | 72 |
| GetMaximumProcessorCount(ALL_PROCESSOR_GROUPS) | 64 | 72 |
| boost::thread::hardware_concurrency() | 32 | 36 |
| Performance counter API | 36 | 36 |
| WMI | 72 | 72 |
| HARDWARE\DESCRIPTION\System\CentralProcessor | 72 | 72 |
+------------------------------------------------+----------------+----------------+
I do not explain why all these functions return different values. The only 2 methods which seem reliable to me is either using WMI (but fairly complicated) or simply to read in the Windows registry the following key: HARDWARE\DESCRIPTION\System\CentralProcessor.
What do you think?
Do you confirm that the WMI and registry key methods are the only reliable methods?
Thanks in advance
The API function that you need is GetLogicalProcessorInformationEx. Since you have more than 64 processors, your processors are grouped. GetLogicalProcessorInformation only reports the processors in the processor group that the thread is currently assigned. You need to use GetLogicalProcessorInformationEx to get past that limitation.
The documentation says:
On systems with more than 64 logical processors, the GetLogicalProcessorInformation function retrieves logical processor information about processors in the processor group to which the calling thread is currently assigned. Use the GetLogicalProcessorInformationEx function to retrieve information about processors in all processor groups on the system.
Late answer with code:
size_t myHardwareConcurrency(){
size_t concurrency=0;
DWORD length=0;
if(GetLogicalProcessorInformationEx(RelationAll,nullptr,&length)!=FALSE){
return concurrency;}
if(GetLastError()!=ERROR_INSUFFICIENT_BUFFER){
return concurrency;}
std::unique_ptr<void,void(*)(void*)>buffer(std::malloc(length),std::free);
if(!buffer){
return concurrency;}
unsigned char*mem=reinterpret_cast<unsigned char*>(buffer.get());
if(GetLogicalProcessorInformationEx(RelationAll,reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(mem),&length)==false){
return concurrency;}
for(DWORD i=0;i<length;){
auto*proc=reinterpret_cast<PSYSTEM_LOGICAL_PROCESSOR_INFORMATION_EX>(mem+i);
if(proc->Relationship==RelationProcessorCore){
for(WORD group=0;group<proc->Processor.GroupCount;++group){
for(KAFFINITY mask=proc->Processor.GroupMask[group].Mask;mask!=0;mask>>=1){
concurrency+=mask&1;}}}
i+=proc->Size;}
return concurrency;}
It worked on my dual Xeon gold 6154 with Windows 64 bit system (2 procs * 18 cores/proc * 2 threads/core = 72 threads). The result is 72 both for 32 bit processes and for 64 bit processes.
I do not have access to a system with a 32 bit Windows though.
In case of error, it returns zero like std::thread::hardware_concurrency does.
You can use the CPUID instruction to query the processor directly (platform independent, though since you can't do inline asm in MSVC anymore for some compilers you'll need to use different functions to have access to it). The only downside is that as of a few years ago Intel and AMD handle this instruction differently, and you'll need to do a lot of work to ensure you are reading the information correctly. In fact, not only will you be able to get a core count, but you can get all kinds of processor topology information. Not sure how it works in a VM though if you are using that environment.
I'm experimenting with the tsx extensions in haswell, by adapting an existing medium-sized (1000's of lines) codebase to using GCC transactional memory extensions (which indirectly are using haswell tsx in this machine) instead of coarse grained locks. I am using GCC's transactional_memory extensions, not writing my own _xbegin / _xend directly. I am using the ITM_DEFAULT_METHOD=htm
I'm having issues getting it to work fast enough because I get high rates of hardware transaction abort for mysterious reasons. As shown below, these aborts are not due to conflicts nor due to capacity limitations.
Here is the perf command I used to quantify the failure rate and underlying causes:
perf stat \
-e cpu/event=0x54,umask=0x2,name=tx_mem_abort_capacity_write/ \
-e cpu/event=0x54,umask=0x1,name=tx_mem_abort_conflict/ \
-e cpu/event=0x5d,umask=0x1,name=tx_exec_misc1/ \
-e cpu/event=0x5d,umask=0x2,name=tx_exec_misc2/ \
-e cpu/event=0x5d,umask=0x4,name=tx_exec_misc3/ \
-e cpu/event=0x5d,umask=0x8,name=tx_exec_misc4/ \
-e cpu/event=0x5d,umask=0x10,name=tx_exec_misc5/ \
-e cpu/event=0xc9,umask=0x1,name=rtm_retired_start/ \
-e cpu/event=0xc9,umask=0x2,name=rtm_retired_commit/ \
-e cpu/event=0xc9,umask=0x4,name=rtm_retired_aborted/pp \
-e cpu/event=0xc9,umask=0x8,name=rtm_retired_aborted_misc1/ \
-e cpu/event=0xc9,umask=0x10,name=rtm_retired_aborted_misc2/ \
-e cpu/event=0xc9,umask=0x20,name=rtm_retired_aborted_misc3/ \
-e cpu/event=0xc9,umask=0x40,name=rtm_retired_aborted_misc4/ \
-e cpu/event=0xc9,umask=0x80,name=rtm_retired_aborted_misc5/ \
./myprogram -th 1 -reps 3000000
So, the program runs some code with transactions in it 30 million times. Each request involves one transaction gcc __transaction_atomic block. There is only one thread in this run.
This particular perf command captures most of the relevant tsx performance events described in the Intel software developers manual vol 3.
The output from perf stat is the following:
0 tx_mem_abort_capacity_write [26.66%]
0 tx_mem_abort_conflict [26.65%]
29,937,894 tx_exec_misc1 [26.71%]
0 tx_exec_misc2 [26.74%]
0 tx_exec_misc3 [26.80%]
0 tx_exec_misc4 [26.92%]
0 tx_exec_misc5 [26.83%]
29,906,632 rtm_retired_start [26.79%]
0 rtm_retired_commit [26.70%]
29,985,423 rtm_retired_aborted [26.66%]
0 rtm_retired_aborted_misc1 [26.75%]
0 rtm_retired_aborted_misc2 [26.73%]
29,927,923 rtm_retired_aborted_misc3 [26.71%]
0 rtm_retired_aborted_misc4 [26.69%]
176 rtm_retired_aborted_misc5 [26.67%]
10.583607595 seconds time elapsed
As you can see from the output:
The rtm_retired_start count is 30 million (matches input to program)
The rtm_retired_abort count is about the same (no commits at all)
The abort_conflict and abort_capacity counts are 0, so these are not the reasons. Also, recall it is only one thread running, conflicts should be rare.
The only actual leads here are the high values of tx_exec_misc1 and rtm_retired_aborted_misc3, which are somewhat similar in description.
The Intel manual (vol 3) defines rtm_retired_aborted_misc3 counters:
code: C9H 20H
mnemonic: RTM_RETIRED.ABORTED_MISC3
description: Number of times an RTM execution aborted due to HLE unfriendly instructions.
The definition for tx_exec_misc1 has some similar words:
code: 5DH 01H
mnemonic: TX_EXEC.MISC1
description: Counts the number of times a class of instructions that may cause a transactional abort was executed. Since this is the count of execution, it may not always cause a transactional abort.
I checked the assembly location for the aborts using perf record/ perf report using high precision (PEBS) support for rtm_retired_aborted. The location has a mov instruction from register to register. No weird instruction names seen nearby.
Update:
Here are two things I've tried since then:
1) the tx_exec_misc1 and rtm_retired_aborted_misc3 signature we we see here can be obtained, for example, by a dummy block of the form
for (int i = 0; i < 10000000; i++){
__transaction_atomic{
_xabort(1);
}
}
or one of the form
for (int i = 0; i < 10000000; i++){
__transaction_atomic{
printf("hello");
fflush(stdout);
}
}
In both cases, the perf counters look similar to what I see. However, in both cases the perf report for -e cpu/tx-abort/ points to the intuitively correct assembly lines: an xabort instruction for the first example and a syscall one for the second one. In the real codebase, the perf report points to a stack push right at the start of a function:
: 00000000004167e0 <myns::myfun()>:
100.00 : 4167e0: push %rbp
0.00 : 4167e1: mov %rsp,%rbp
0.00 : 4167e4: push %r15
I have also run the same command under the intel software development emulator. It turns out that the problem goes away in that case: I get no aborts as far as the application is concerned.
Though it's been the case for a while, I found this unanswered question while searching, so here's the answer: This is a hardware bug in Haswell and early Broadwell chips.
The particular hardware erratum assigned by Intel is HSW136, and is not fixable using microcode updates. Indeed, I think it was in stepping 4 that the feature was no longer reported as available by the cpuid instruction, even when there was (faulty) silicon on the chip to implement it.
I would like to write a program that makes extensive use of BLAS and LAPACK linear algebra functionalities. Since performance is an issue I did some benchmarking and would like know, if the approach I took is legitimate.
I have, so to speak, three contestants and want to test their performance with a simple matrix-matrix multiplication. The contestants are:
Numpy, making use only of the functionality of dot.
Python, calling the BLAS functionalities through a shared object.
C++, calling the BLAS functionalities through a shared object.
Scenario
I implemented a matrix-matrix multiplication for different dimensions i. i runs from 5 to 500 with an increment of 5 and the matricies m1 and m2 are set up like this:
m1 = numpy.random.rand(i,i).astype(numpy.float32)
m2 = numpy.random.rand(i,i).astype(numpy.float32)
1. Numpy
The code used looks like this:
tNumpy = timeit.Timer("numpy.dot(m1, m2)", "import numpy; from __main__ import m1, m2")
rNumpy.append((i, tNumpy.repeat(20, 1)))
2. Python, calling BLAS through a shared object
With the function
_blaslib = ctypes.cdll.LoadLibrary("libblas.so")
def Mul(m1, m2, i, r):
no_trans = c_char("n")
n = c_int(i)
one = c_float(1.0)
zero = c_float(0.0)
_blaslib.sgemm_(byref(no_trans), byref(no_trans), byref(n), byref(n), byref(n),
byref(one), m1.ctypes.data_as(ctypes.c_void_p), byref(n),
m2.ctypes.data_as(ctypes.c_void_p), byref(n), byref(zero),
r.ctypes.data_as(ctypes.c_void_p), byref(n))
the test code looks like this:
r = numpy.zeros((i,i), numpy.float32)
tBlas = timeit.Timer("Mul(m1, m2, i, r)", "import numpy; from __main__ import i, m1, m2, r, Mul")
rBlas.append((i, tBlas.repeat(20, 1)))
3. c++, calling BLAS through a shared object
Now the c++ code naturally is a little longer so I reduce the information to a minimum.
I load the function with
void* handle = dlopen("libblas.so", RTLD_LAZY);
void* Func = dlsym(handle, "sgemm_");
I measure the time with gettimeofday like this:
gettimeofday(&start, NULL);
f(&no_trans, &no_trans, &dim, &dim, &dim, &one, A, &dim, B, &dim, &zero, Return, &dim);
gettimeofday(&end, NULL);
dTimes[j] = CalcTime(start, end);
where j is a loop running 20 times. I calculate the time passed with
double CalcTime(timeval start, timeval end)
{
double factor = 1000000;
return (((double)end.tv_sec) * factor + ((double)end.tv_usec) - (((double)start.tv_sec) * factor + ((double)start.tv_usec))) / factor;
}
Results
The result is shown in the plot below:
Questions
Do you think my approach is fair, or are there some unnecessary overheads I can avoid?
Would you expect that the result would show such a huge discrepancy between the c++ and python approach? Both are using shared objects for their calculations.
Since I would rather use python for my program, what could I do to increase the performance when calling BLAS or LAPACK routines?
Download
The complete benchmark can be downloaded here. (J.F. Sebastian made that link possible^^)
UPDATE (30.07.2014):
I re-run the the benchmark on our new HPC.
Both the hardware as well as the software stack changed from the setup in the original answer.
I put the results in a google spreadsheet (contains also the results from the original answer).
Hardware
Our HPC has two different nodes one with Intel Sandy Bridge CPUs and one with the newer Ivy Bridge CPUs:
Sandy (MKL, OpenBLAS, ATLAS):
CPU: 2 x 16 Intel(R) Xeon(R) E2560 Sandy Bridge # 2.00GHz (16 Cores)
RAM: 64 GB
Ivy (MKL, OpenBLAS, ATLAS):
CPU: 2 x 20 Intel(R) Xeon(R) E2680 V2 Ivy Bridge # 2.80GHz (20 Cores, with HT = 40 Cores)
RAM: 256 GB
Software
The software stack is for both nodes the sam. Instead of GotoBLAS2, OpenBLAS is used and there is also a multi-threaded ATLAS BLAS that is set to 8 threads (hardcoded).
OS: Suse
Intel Compiler: ictce-5.3.0
Numpy: 1.8.0
OpenBLAS: 0.2.6
ATLAS:: 3.8.4
Dot-Product Benchmark
Benchmark-code is the same as below. However for the new machines I also ran the benchmark for matrix sizes 5000 and 8000.
The table below includes the benchmark results from the original answer (renamed: MKL --> Nehalem MKL, Netlib Blas --> Nehalem Netlib BLAS, etc)
Single threaded performance:
Multi threaded performance (8 threads):
Threads vs Matrix size (Ivy Bridge MKL):
Benchmark Suite
Single threaded performance:
Multi threaded (8 threads) performance:
Conclusion
The new benchmark results are similar to the ones in the original answer. OpenBLAS and MKL perform on the same level, with the exception of Eigenvalue test.
The Eigenvalue test performs only reasonably well on OpenBLAS in single threaded mode.
In multi-threaded mode the performance is worse.
The "Matrix size vs threads chart" also show that although MKL as well as OpenBLAS generally scale well with number of cores/threads,it depends on the size of the matrix. For small matrices adding more cores won't improve performance very much.
There is also approximately 30% performance increase from Sandy Bridge to Ivy Bridge which might be either due to higher clock rate (+ 0.8 Ghz) and/or better architecture.
Original Answer (04.10.2011):
Some time ago I had to optimize some linear algebra calculations/algorithms which were written in python using numpy and BLAS so I benchmarked/tested different numpy/BLAS configurations.
Specifically I tested:
Numpy with ATLAS
Numpy with GotoBlas2 (1.13)
Numpy with MKL (11.1/073)
Numpy with Accelerate Framework (Mac OS X)
I did run two different benchmarks:
simple dot product of matrices with different sizes
Benchmark suite which can be found here.
Here are my results:
Machines
Linux (MKL, ATLAS, No-MKL, GotoBlas2):
OS: Ubuntu Lucid 10.4 64 Bit.
CPU: 2 x 4 Intel(R) Xeon(R) E5504 # 2.00GHz (8 Cores)
RAM: 24 GB
Intel Compiler: 11.1/073
Scipy: 0.8
Numpy: 1.5
Mac Book Pro (Accelerate Framework):
OS: Mac OS X Snow Leopard (10.6)
CPU: 1 Intel Core 2 Duo 2.93 Ghz (2 Cores)
RAM: 4 GB
Scipy: 0.7
Numpy: 1.3
Mac Server (Accelerate Framework):
OS: Mac OS X Snow Leopard Server (10.6)
CPU: 4 X Intel(R) Xeon(R) E5520 # 2.26 Ghz (8 Cores)
RAM: 4 GB
Scipy: 0.8
Numpy: 1.5.1
Dot product benchmark
Code:
import numpy as np
a = np.random.random_sample((size,size))
b = np.random.random_sample((size,size))
%timeit np.dot(a,b)
Results:
System | size = 1000 | size = 2000 | size = 3000 |
netlib BLAS | 1350 ms | 10900 ms | 39200 ms |
ATLAS (1 CPU) | 314 ms | 2560 ms | 8700 ms |
MKL (1 CPUs) | 268 ms | 2110 ms | 7120 ms |
MKL (2 CPUs) | - | - | 3660 ms |
MKL (8 CPUs) | 39 ms | 319 ms | 1000 ms |
GotoBlas2 (1 CPU) | 266 ms | 2100 ms | 7280 ms |
GotoBlas2 (2 CPUs)| 139 ms | 1009 ms | 3690 ms |
GotoBlas2 (8 CPUs)| 54 ms | 389 ms | 1250 ms |
Mac OS X (1 CPU) | 143 ms | 1060 ms | 3605 ms |
Mac Server (1 CPU)| 92 ms | 714 ms | 2130 ms |
Benchmark Suite
Code:
For additional information about the benchmark suite see here.
Results:
System | eigenvalues | svd | det | inv | dot |
netlib BLAS | 1688 ms | 13102 ms | 438 ms | 2155 ms | 3522 ms |
ATLAS (1 CPU) | 1210 ms | 5897 ms | 170 ms | 560 ms | 893 ms |
MKL (1 CPUs) | 691 ms | 4475 ms | 141 ms | 450 ms | 736 ms |
MKL (2 CPUs) | 552 ms | 2718 ms | 96 ms | 267 ms | 423 ms |
MKL (8 CPUs) | 525 ms | 1679 ms | 60 ms | 137 ms | 197 ms |
GotoBlas2 (1 CPU) | 2124 ms | 4636 ms | 147 ms | 456 ms | 743 ms |
GotoBlas2 (2 CPUs)| 1560 ms | 3278 ms | 116 ms | 295 ms | 460 ms |
GotoBlas2 (8 CPUs)| 741 ms | 2914 ms | 82 ms | 262 ms | 192 ms |
Mac OS X (1 CPU) | 948 ms | 4339 ms | 151 ms | 318 ms | 566 ms |
Mac Server (1 CPU)| 1033 ms | 3645 ms | 99 ms | 232 ms | 342 ms |
Installation
Installation of MKL included installing the complete Intel Compiler Suite which is pretty straight forward. However because of some bugs/issues configuring and compiling numpy with MKL support was a bit of a hassle.
GotoBlas2 is a small package which can be easily compiled as a shared library. However because of a bug you have to re-create the shared library after building it in order to use it with numpy.
In addition to this building it for multiple target plattform didn't work for some reason. So I had to create an .so file for each platform for which i want to have an optimized libgoto2.so file.
If you install numpy from Ubuntu's repository it will automatically install and configure numpy to use ATLAS. Installing ATLAS from source can take some time and requires some additional steps (fortran, etc).
If you install numpy on a Mac OS X machine with Fink or Mac Ports it will either configure numpy to use ATLAS or Apple's Accelerate Framework.
You can check by either running ldd on the numpy.core._dotblas file or calling numpy.show_config().
Conclusions
MKL performs best closely followed by GotoBlas2.
In the eigenvalue test GotoBlas2 performs surprisingly worse than expected. Not sure why this is the case.
Apple's Accelerate Framework performs really good especially in single threaded mode (compared to the other BLAS implementations).
Both GotoBlas2 and MKL scale very well with number of threads. So if you have to deal with big matrices running it on multiple threads will help a lot.
In any case don't use the default netlib blas implementation because it is way too slow for any serious computational work.
On our cluster I also installed AMD's ACML and performance was similar to MKL and GotoBlas2. I don't have any numbers tough.
I personally would recommend to use GotoBlas2 because it's easier to install and it's free.
If you want to code in C++/C also check out Eigen3 which is supposed to outperform MKL/GotoBlas2 in some cases and is also pretty easy to use.
I've run your benchmark. There is no difference between C++ and numpy on my machine:
Do you think my approach is fair, or are there some unnecessary overheads I can avoid?
It seems fair due to there is no difference in results.
Would you expect that the result would show such a huge discrepancy between the c++ and python approach? Both are using shared objects for their calculations.
No.
Since I would rather use python for my program, what could I do to increase the performance when calling BLAS or LAPACK routines?
Make sure that numpy uses optimized version of BLAS/LAPACK libraries on your system.
Here's another benchmark (on Linux, just type make): http://dl.dropbox.com/u/5453551/blas_call_benchmark.zip
http://dl.dropbox.com/u/5453551/blas_call_benchmark.png
I do not see essentially any difference between the different methods for large matrices, between Numpy, Ctypes and Fortran. (Fortran instead of C++ --- and if this matters, your benchmark is probably broken.)
Your CalcTime function in C++ seems to have a sign error. ... + ((double)start.tv_usec)) should be instead ... - ((double)start.tv_usec)). Perhaps your benchmark also has other bugs, e.g., comparing between different BLAS libraries, or different BLAS settings such as number of threads, or between real time and CPU time?
EDIT: failed to count the braces in the CalcTime function -- it's OK.
As a guideline: if you do a benchmark, please always post all the code somewhere. Commenting on benchmarks, especially when surprising, without having the full code is usually not productive.
To find out which BLAS Numpy is linked against, do:
$ python
Python 2.7.2+ (default, Aug 16 2011, 07:24:41)
[GCC 4.6.1] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy.core._dotblas
>>> numpy.core._dotblas.__file__
'/usr/lib/pymodules/python2.7/numpy/core/_dotblas.so'
>>>
$ ldd /usr/lib/pymodules/python2.7/numpy/core/_dotblas.so
linux-vdso.so.1 => (0x00007fff5ebff000)
libblas.so.3gf => /usr/lib/libblas.so.3gf (0x00007fbe618b3000)
libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007fbe61514000)
UPDATE: If you can't import numpy.core._dotblas, your Numpy is using its internal fallback copy of BLAS, which is slower, and not meant to be used in performance computing!
The reply from #Woltan below indicates that this is the explanation for the difference he/she sees in Numpy vs. Ctypes+BLAS.
To fix the situation, you need either ATLAS or MKL --- check these instructions: http://scipy.org/Installing_SciPy/Linux Most Linux distributions ship with ATLAS, so the best option is to install their libatlas-dev package (name may vary).
Given the rigor you've shown with your analysis, I'm surprised by the results thus far. I put this as an 'answer' but only because it's too long for a comment and does provide a possibility (though I expect you've considered it).
I would've thought the numpy/python approach wouldn't add much overhead for a matrix of reasonable complexity, since as the complexity increases, the proportion that python participates in should be small. I'm more interested in the results on the right hand side of the graph, but orders of magnitude discrepancy shown there would be disturbing.
I wonder if you're using the best algorithms that numpy can leverage. From the compilation guide for linux:
"Build FFTW (3.1.2):
SciPy Versions >= 0.7 and Numpy >= 1.2:
Because of license, configuration, and maintenance issues support for FFTW was removed in versions of SciPy >= 0.7 and NumPy >= 1.2. Instead now uses a built-in version of fftpack.
There are a couple ways to take advantage of the speed of FFTW if necessary for your analysis.
Downgrade to a Numpy/Scipy version that includes support.
Install or create your own wrapper of FFTW. See http://developer.berlios.de/projects/pyfftw/ as an un-endorsed example."
Did you compile numpy with mkl? (http://software.intel.com/en-us/articles/intel-mkl/). If you're running on linux, the instructions for compiling numpy with mkl are here: http://www.scipy.org/Installing_SciPy/Linux#head-7ce43956a69ec51c6f2cedd894a4715d5bfff974 (in spite of url). The key part is:
[mkl]
library_dirs = /opt/intel/composer_xe_2011_sp1.6.233/mkl/lib/intel64
include_dirs = /opt/intel/composer_xe_2011_sp1.6.233/mkl/include
mkl_libs = mkl_intel_lp64,mkl_intel_thread,mkl_core
If you're on windows, you can obtain a compiled binary with mkl, (and also obtain pyfftw, and many other related algorithms) at: http://www.lfd.uci.edu/~gohlke/pythonlibs/, with a debt of gratitude to Christoph Gohlke at the Laboratory for Fluorescence Dynamics, UC Irvine.
Caveat, in either case, there are many licensing issues and so on to be aware of, but the intel page explains those. Again, I imagine you've considered this, but if you meet the licensing requirements (which on linux is very easy to do), this would speed up the numpy part a great deal relative to using a simple automatic build, without even FFTW. I'll be interested to follow this thread and see what others think. Regardless, excellent rigor and excellent question. Thanks for posting it.
Let me contribute to a somewhat weird finding
My numpy is linked to mkl, as given by numpy.show_config(). I have no idea what kind of libblas.so C++/BLAS was used. I hope someone can tell me a way to figure it out.
I think the results strongly depend on the library was used. I cannot isolate the efficiency of C++/BLAS.
I have an application that interacts with external devices using serial communication. There are two versions of the device differing in their implementations.
-->One is developed and tested by my team
-->The other version by a different team.
Since the other team has left, our team is looking after it's maintenance. The other day while testing the application I noticed that the application takes up 60 Mb memory at startup and to my horror it's memory usage starts increasing with 200Kb chunks, in 60 hrs it shoots up to 295 Mb though there is no slow down in the responsiveness and usage of application. I tested it again and again and the same memory usage pattern is repeated.
The application is made in C++,Qt 4.2.1 on RHEL4.
I used mtrace to check for any memory leaks and it shows no such leaks. I then used valgrind memcheck tool, but the messages it gives are cryptic and not very conclusive, it shows leaks in graphical elements of Qt, which on scrutiny can be straightaway rejected.
I am in a fix as to what other tools/methodologies can be adopted to pinpoint the source of these memory leaks if any.
-->Also, in a larger context, how can we detect and debug presence of memory leaks in a C++ Qt application?
-->How can we check, how much memory a process uses in Linux?
I had used gnome-system-monitor and top command to check for memory used by the application, but I have heard that results given by above mentioned tools are not absolute.
EDIT:
I used ccmalloc for detecting memory leaks and this is the error report I got after I closed the application. During application execution, there were no error messages.
|ccmalloc report|
=======================================================
| total # of| allocated | deallocated | garbage |
+-----------+-------------+-------------+-------------+
| bytes| 387325257 | 386229435 | 1095822 |
+-----------+-------------+-------------+-------------+
|allocations| 1232496 | 1201351 | 31145 |
+-----------------------------------------------------+
| number of checks: 1 |
| number of counts: 2434332 |
| retrieving function names for addresses ... done. |
| reading file info from gdb ... done. |
| sorting by number of not reclaimed bytes ... done. |
| number of call chains: 3 |
| number of ignored call chains: 0 |
| number of reported call chains: 3 |
| number of internal call chains: 3 |
| number of library call chains: 1 |
=======================================================
|
| 3.1% = 33.6 KB of garbage allocated in 47 allocations
| |
| | 0x???????? in
| |
| | 0x081ef2b6 in
| | at src/wrapper.c:489
| |
| | 0x081ef169 in <_realloc>
| | at src/wrapper.c:435
| |
| `-----> 0x081ef05c in
| at src/wrapper.c:318
|
| 0.8% = 8722 Bytes of garbage allocated in 35 allocations
| |
| | 0x???????? in
| |
| | 0x081ef134 in
| | at src/wrapper.c:422
| |
| `-----> 0x081ef05c in
| at src/wrapper.c:318
|
| 0.1% = 1144 Bytes of garbage allocated in 5 allocations
| |
| | 0x???????? in
| |
| | 0x081ef1cb in
| | at src/wrapper.c:455
| |
| `-----> 0x081ef05c in
| at src/wrapper.c:318
|
`------------------------------------------------------
free(0x09cb650c) after reporting
(This can happen with static destructors.
When linking put `ccmalloc.o' at the end (for gcc) or
in front of the list of object files.)
free(0x09cb68f4) after reporting
free(0x09cb68a4) after reporting
free(0x09cb6834) after reporting
free(0x09cb6814) after reporting
free(0x09cb67a4) after reporting
free(0x09cb6784) after reporting
free(0x09cb66cc) after reporting
free(0x09cb66ac) after reporting
free(0x09cb65e4) after reporting
free(0x09cb65c4) after reporting
free(0x09cb653c) after reporting
ccmalloc_report() called in non valid state
I have no clue, what this means, it doesn't seem to indicate any memory leaks to me? I may be wrong. Does anyone of you have come across such a scenario?
link|edit|delete
Valgrind can be a bitch if you don't really read the manuals or whatever documentation is actually available (man page for starters) - but they are worth it.
Basicly, you could start by running the valgrind on your application with --gen-suppressions=all and then create a suppressions for each block that is originating from QT itself and then use the suppression file to block those errors and you should be left with only with errors in your own code.
Also, you could try to use valgrind thru a alleyoop frontend if that makes things easier for you.
There are also bunch of other tools that can be used to detect memory leaks and Linux Journal has article about those here: http://www.linuxjournal.com/article/6556
And last, in some cases, some static analysis tools can spot memory errors too..
I'd like to make the minor point that just because the meory used by a process is increasing, it does not follow that you have a memory leak. Take a word processor as an example - as you write text, the memory usage increases, but there is no leak. Most processes in fact increase their memoryy usage as they run, often until they reach some sort of near steady-state, where objects been created are balanced by old objects being destroyed.
You said you tried Valgrind's memcheck tool; you should also try the massif tool, which should be able to graph the heap usage over time, and tell you where the memory was allocated from.
One of the reasons why top isn't too useful to measure memory usage is that they don't take into account that memory is often shared between processes. For the best overview on where the process has allocated memory, I recommend using a recent Linux kernel and checking /proc/<pid>/maps for your process. This shows what memory is mapped to that process and from where. For example, here's a snippet from konqueror on my system.
b732a000-b7a20000 r-xp 00000000 fd:05 205437 /usr/lib/qt3/lib/libqt-mt.so.3.3.8
Size: 7128 kB
Rss: 3456 kB
Pss: 347 kB
Shared Clean: 3452 kB
Shared Dirty: 0 kB
Private Clean: 4 kB
Private Dirty: 0 kB
Referenced: 3452 kB
The important thing here is that, although the resident set resulting from the load of libqt-mt.so.3.3.8 is 3456kB, all but 4kB of that is shared between all processes which loaded the library, so it's a one-off system-wide cost. top doesn't expose this information, so just reading the RSS from top is misleading.