I was wondering if someone has had experience with the llvm/tools - lli interpreter/JIT-compiler (cf. http://llvm.org/docs/GettingStarted.html#tools). I am interested in any information that you can provide (speed, complexity, implementations, etc.).
Thanks.
UPDATE:
Okay how would bitcode execution be compared to LuaJIT VM execution, supposing that lli acts as an interpreter? What about when lli acts as a jit-compiler (same comparison)?
NOTE:
I am only asking if anyone has experience/ is willing to spare some time to share.
LuaJIT is a tracing JIT, which means it can re-optimize itself to better suite the data passed through the execution environment, however, LLVM is a static JIT, and thus will just generate the once-off best-case machine code for the corresponding source, which may leading it it loosing performance in tight loops or bad branch misspredictions.
The actual LuaJIT VM is also highly optimized, threaded, machine specific assembly, where as LLVM uses C++ for portability (and other reasons), so this obviously gives LuaJIT a huge advantage. LLVM also has a much higher overhead than LuaJIT, purely because LuaJIT was designed to work on much less powerful systems (such as those driven by ARM CPU's).
The LuaJIT bytecode was also specially designed for LuaJIT, where as LLVM's bitcode is a lot more generic, this will obviously make LuaJIT's execute faster. LuaJIT's bytecode is also well designed for encoding optimization hints etc for use by the JIT and the tracer.
ignoring the fact that these are two different types of JITs, the whole comparison boils down to LLVM is focused on being a generic JIT/Compiler backend, LuaJIT is focused on executing Lua as fast as possible in the best way possible, thus it gains from not being constrained by generality.
Related
I want some tool to diagnose use-after-free bugs and uninitialized bugs. I am considering Sanitizer(Memory and/or Address) and Valgrind. But I have very little idea about their advantages and disadvantages. Can anyone tell the main features, differences and pros/cons of Sanitizer and Valgrind?
Edit: I found some of comparisons like: Valgrind uses DBI(dynamic binary instrumentation) and Sanitizer uses CTI(compile-time instrumentation). Valgrind makes the program much slower(20x) whether Sanitizer runs much faster than Valgrind(2x). If anyone can give me some more important points to consider, it will be a great help.
I think you'll find this wiki useful.
TLDR main advantages of sanitizers are
much smaller CPU overheads (Lsan is practically free, UBsan/Isan is 1.25x, Asan and Msan are 2-4x for computationally intensive tasks and 1.05-1.1x for GUIs, Tsan is 5-15x)
wider class of detected errors (stack and global overflows, use-after-return/scope)
full support of multi-threaded apps (Valgrind support for multi-threading is a joke)
much smaller memory overhead (up to 2x for Asan, up to 3x for Msan, up to 10x for Tsan which is way better than Valgrind)
Disadvantages are
more complicated integration (you need to teach your build system to understand Asan and sometimes work around limitations/bugs in Asan itself, you also need to use relatively recent compiler)
MemorySanitizer is not reall^W easily usable at the moment as it requires one to rebuild all dependencies under Msan (including all standard libraries e.g. libc++); this means that casual users can only use Valgrind for detecting uninitialized errors
sanitizers typically can not be combined with each other (the only supported combination is Asan+UBsan+Lsan) which means that you'll have to do separate QA runs to catch all types of bugs
One big difference is that the LLVM-included memory and thread sanitizers implicitly map huge swathes of address space (e.g., by calling mmap(X, Y, 0, MAP_NORESERVE|MAP_ANONYMOUS|MAP_FIXED|MAP_PRIVATE, -1, 0) across terabytes of address space in the x86_64 environment). Even though they don't necessarily allocate that memory, the mapping can play havoc with restrictive environments (e.g., ones with reasonable settings for ulimit values).
I am interested knowing the best approach for bulk memory copies on an x86 architecture. I realize this depends on machine-specific characteristics. The main target is typical desktop machines made in the last 4-5 years.
I know that in the old days MOVSD with REPE was nominally the fastest approach because you could move 4 bytes at a time, but I have read that nowadays MOVSB is just as fast and is simpler to write, so you may as well do a byte move and just forget about the complexities of a 4-byte move.
A surrounding question is whether MOVxx instructions are worth it at all. If the CPU can run so much faster than the memory bus, then maybe it is pointless to use a CISC move and you may as well use a plain MOV. This would be most attractive because then I could use the same algorithms on other processor architectures like ARM. This brings up the analogous question of whether ARM's specialized instructions for bulk memory moves (which are totally different than Intels) are worth it or not.
Note: I have read section 3.7.6 in the Intel Optimization Reference Manual so I am familiar with the basics. I am hoping someone can relate practical experience in the area beyond what is in this manual.
Modern Intel and AMD processors have optimisations on REP MOVSB that make it copy entire cache lines at a time if it can, making it the best (may not be fastest, but pretty close) method of copying bulk data.
As for ARM, it depends on the architecture version, but in general using an unrolled loop would be the most efficient.
Back in the days before caches and branch prediction, it was relatively common if not encouraged to make self-modifying code for certain kinds of optimizations. It was probably most common in games and demos written in assembler in the eras between 8-bit up to early 32-bit such as the Amiga.
I'm not sure if any compilers from those days emitted self-modifying assembler or machine code.
What I'm wondering is whether there are any current/modern compilers that do. Obviously it would be useless or too difficult on powerful processors with caches.
But What about the very many simple processors such as used in embedded systems? Is self modifying code considered a viable optimization strategy by any modern compilers for any simple / 8-bit / embedded processor?
There is a question with a similar title, "Is there any self-improving compiler around?
", but note that it's not about the same subject:
Please note that I am talking about a compiler that improves itself - not a compiler that improves the code it compiles.
All embedded systems today use flash ROM. I believe Amiga and similar were RAM-based systems. The only way "self-modification" exists in embedded systems, is where you have boot loaders, that re-program certain parts of the flash depending on what program and/or functionality that should be used.
Apart from that, it doesn't make sense for the program to modify itself in runtime. Running code from RAM is generally discouraged, for safety reasons (potential of accidental modifications caused by bugs) and for electric reasons (RAM is volatile and far more sensitive to EMC than flash).
Please post the points one should keep in mind while designing or coding for lesser footprint deliverables for embedded systems.
I am not giving compiler or platform details, as I want generic information. But, any specific information on Linux based OS is also welcome.
Depends on how low you want to get. I'm currently coding for fiscal printers, and there's no OS, and the main rule is no dynamic memory allocation. The funny thing is that I still convinced the crew to code fully modern C++ ;).
Actually there are a few rules we decided upon:
no dynamic allocation
hence, no STL
no exception handling (obvious reasons)
There isn't a general answer, only ones specific to language/platform ... but
Small memory footprint ...
Don't use Java, C#/mono, PHP, Perl, Python or anything with garbage collection
Get as close to the metal as feasible, Use C
Do alot of profiling to see where memory is getting allocated, if you are using dynamic allocation
Ensure you prevent heap-fragmentation by allocating sensible chunks and sizes of the heap
Avoid recursive functions especially those that use malloc(). Better allocating a chunk and passing a pointer around.
use free() ;)
Ensure your types are no bigger than required
Turn on compiler optimizations
There will be more.
for real low footprint consider doing Assembly directly.
We all know that Hello World in C or C++ is 20kb+(because of all the default libraries which get linked). In Assembly this overhead is gone. As pointed out in the comments one can reduce the standard libraries quite a bit. However, the fact remains that the code density you can get when coding assembly is much higher than a compiler will generate from a higher language. So for code where every byte matters, use assembly.
also when programming on devices with less capable processors, programming in assembly language might be your only way to do make the program fast enough for it to be realtime enough to (for instance) control machines
When faced with such constraints, it is advisable to pre-allocate memory in order to guarantee that the system will work under load. A design pattern such as "object pooling" can be used to share resources within the system.
The C language enables tight resource (i.e. memory & compute cycles) control. It should be strongly considered.
Avoid recursion as it is easy to abuse and can result in stack overflow conditions.
(i) If a Program is optimised for one CPU class (e.g. Multi-Core Core i7)
by compiling the Code on the same , then will its performance
be at sub-optimal level on other CPUs from older generations (e.g. Pentium 4)
... Optimizing may prove harmful for performance on other CPUs..?
(ii)For optimization, compilers may use x86 extensions (like SSE 4) which are
not available in older CPUs.... so ,Is there a fall-back to some non-extensions
based routine on older CPUs..?
(iii)Is Intel C++ Compiler is more optimizing than Visual C++ Compiler or GCC..
(iv) Will a truly Multi-Core Threaded application will perform effeciently on a
older CPUs (like Pentium III or 4)..?
Compiling on a platform does not mean optimizing for this platform. (maybe it's just bad wording in your question.)
In all compilers I've used, optimizing for platform X does not affect the instruction set, only how it is used, e.g. optimizing for i7 does not enable SSE2 instructions.
Also, optimizers in most cases avoid "pessimizing" non-optimized platforms, e.g. when optimizing for i7, typically a small improvement on i7 will not not be chosen if it means a major hit for another common platform.
It also depends in the performance differences in the instruction sets - my impression is that they've become much less in the last decade (but I haven't delved to deep lately - might be wrong for the latest generations). Also consider that optimizations make a notable difference only in few places.
To illustrate possible options for an optimizer, consider the following methods to implement a switch statement:
sequence if (x==c) goto label
range check and jump table
binary search
combination of the above
the "best" algorithm depends on the relative cost of comparisons, jumps by fixed offsets and jumps to an address read from memory. They don't differ much on modern platforms, but even small differences can create a preference for one or other implementation.
It is probably true that optimising code for execution on CPU X will make that code less optimal on CPU Y than the same code optimised for execution on CPU Y. Probably.
Probably not.
Impossible to generalise. You have to test your code and come to your own conclusions.
Probably not.
For every argument about why X should be faster than Y under some set of conditions (choice of compiler, choice of CPU, choice of optimisation flags for compilation) some clever SOer will find a counter-argument, for every example a counter-example. When the rubber meets the road the only recourse you have is to test and measure. If you want to know whether compiler X is 'better' than compiler Y first define what you mean by better, then run a lot of experiments, then analyse the results.
I) If you did not tell the compiler which CPU type to favor, the odds are that it will be slightly sub-optimal on all CPUs. On the other hand, if you let the compiler know to optimize for your specific type of CPU, then it can definitely be sub-optimal on other CPU types.
II) No (for Intel and MS at least). If you tell the compiler to compile with SSE4, it will feel safe using SSE4 anywhere in the code without testing. It becomes your responsibility to ensure that your platform is capable of executing SSE4 instructions, otherwise your program will crash. You might want to compile two libraries and load the proper one. An alternative to compiling for SSE4 (or any other instruction set) is to use intrinsics, these will check internally for the best performing set of instructions (at the cost of a slight overhead). Note that I am not talking about instruction instrinsics here (those are specific to an instruction set), but intrinsic functions.
III) That is a whole other discussion in itself. It changes with every version, and may be different for different programs. So the only solution here is to test. Just a note though; Intel compilers are known not to compile well for running on anything other than Intel (e.g.: intrinsic functions may not recognize the instruction set of a AMD or Via CPU).
IV) If we ignore the on-die efficiencies of newer CPUs and the obvious architecture differences, then yes it may perform as well on older CPU. Multi-Core processing is not dependent per se on the CPU type. But the performance is VERY dependent on the machine architecture (e.g.: memory bandwidth, NUMA, chip-to-chip bus), and differences in the Multi-Core communication (e.g.: cache coherency, bus locking mechanism, shared cache). All this makes it impossible to compare newer and older CPU efficiencies in MP, but that is not what you are asking I believe. So on the whole, a MP program made for newer CPUs, should not be using less efficiently the MP aspects of older CPUs. Or in other words, just tweaking the MP aspects of a program specifically for an older CPU will not do much. Obviously you could rewrite your algorithm to more efficiently use a specific CPU (e.g.: A shared cache may permit you to use an algorithm that exchanges more data between working threads, but this algo will die on a system with no shared cache, full bus lock and low memory latency/bandwidth), but it involves a lot more than just MP related tweaks.
(1) Not only is it possible but it has been documented on pretty much every generation of x86 processor. Go back to the 8088 and work your way forward, every generation. Clock for clock the newer processor was slower for the current mainstream applications and operating systems (including Linux). The 32 to 64 bit transition is not helping, more cores and less clock speed is making it even worse. And this is true going backward as well for the same reason.
(2) Bank on your binaries failing or crashing. Sometimes you get lucky, most of the time you dont. There are new instructions yes, and to support them would probably mean trap for an undefined instruction and have a software emulation of that instruction which would be horribly slow and the lack of demand for it means it is probably not well done or just not there. Optimization can use new instructions but more than that the bulk of the optimization that I am guessing you are talking about has to do with reordering the instructions so that the various pipelines do not stall. So you arrange them to be fast on one generation processor they will be slower on another because in the x86 family the cores change too much. AMD had a good run there for a while as they would make the same code just run faster instead of trying to invent new processors that eventually would be faster when the software caught up. No longer true both amd and intel are struggling to just keep chips running without crashing.
(3) Generally, yes. For example gcc is a horrible compiler, one size fits all fits no one well, it can never and will never be any good at optimizing. For example gcc 4.x code is slower on gcc 3.x code for the same processor (yes all of this is subjective, it all depends on the specific application being compiled). The in house compilers I have used were leaps and bounds ahead of the cheap or free ones (I am not limiting myself to x86 here). Are they worth the price though? That is the question.
In general because of the horrible new programming languages and gobs of memory, storage, layers of caching, software engineering skills are at an all time low. Which means the pool of engineers capable of making a good compiler much less a good optimizing compiler decreases with time, this has been going on for at least 10 years. So even the in house compilers are degrading with time, or they just have their employees to work on and contribute to the open source tools instead having an in house tool. Also the tools the hardware engineers use are degrading for the same reason, so we now have processors that we hope to just run without crashing and not so much try to optimize for. There are so many bugs and chip variations that most of the compiler work is avoiding the bugs. Bottom line, gcc has singlehandedly destroyed the compiler world.
(4) See (2) above. Don't bank on it. Your operating system that you want to run this on will likely not install on the older processor anyway, saving you the pain. For the same reason that the binaries optimized for your pentium III ran slower on your Pentium 4 and vice versa. Code written to work well on multi core processors will run slower on single core processors than if you had optimized the same application for a single core processor.
The root of the problem is the x86 instruction set is dreadful. So many far superior instructions sets have come along that do not require hardware tricks to make them faster every generation. But the wintel machine created two monopolies and the others couldnt penetrate the market. My friends keep reminding me that these x86 machines are microcoded such that you really dont see the instruction set inside. Which angers me even more that the horrible isa is just an interpretation layer. It is kinda like using Java. The problems you have outlined in your questions will continue so long as intel stays on top, if the replacement does not become the monopoly then we will be stuck forever in the Java model where you are one side or the other of a common denominator, either you emulate the common platform on your specific hardware, or you are writing apps and compiling to the common platform.