I am planning to write something to take advantages of the many devices that I have at home.
Basically my aim is to use the laptop to execute calculations, and also to use my main desktop computer to add more power (and finish the task quicker). I work with cellular simulation and chemical interactions, so to me would be great to take advantage of all that I have available at home.
I am using mainly OSX, so I need something that may work with that OS. I can code in objective-C, C and C++.
I am aware of GCD, OpenCL and MPI, but I am not sure which way to go.
I was planning to not use the full power of my desktop but only some of the available cores (in this way I can continue to work on the desktop doing other tasks that are not so resource intensive). In particular I would love to use the graphic card power (it is an ATI card, so no CUDA), since all that I do mainly is spreadsheet, word and coding with Xcode, and the graphic card resources are basically unused in that scenario.
Is there a specific set of libraries or API, among the aforementioned 3, that would allow me to selectively route tasks, and use resources on another machine without leaving the control totally to the compiler? I've heard that GCD is great but it has very limited control on where the blocks are executed, while MPI is on the other side of the spectrum....OpenCL seems to be in the middle.
Before diving in one of these technologies I would like to know which one would most likely suit my needs; I am sure that some other researcher has already used successfully parallel computing to achieve what I am trying to achieve.
Thanks in advance.
MPI is more for scientific computing large scale many processors many nodes exc not for a weekend project, for what you describe I would suggest using OpenCl or any one the more distributed framework of AMQP protocol families, such as zeromq or rabbitMQ, or a combination of OpenCl and AMQP , or even simpler consider multithreading , i would suggest OpenMP for that. I'm not sure if you are looking for direct solvers or parallel functions but there are many that exist as well for gpu's and cpu's which you can find on the web
Sorry, but this question simply cannot be meaningfully answered as posed. To be sure, I could toss out a collection of buzzwords describing various technologies to look at like GCD, OpenMPI, OpenCL, CUDA and any number of other technologies which allow one to run a single program on multiple cores, multiple programs on different cooperating computers, or a single program distributed across CPU and GPU, and it sounds like you know about a number of those already so I wouldn't even be adding much value in listing the buzzwords.
To simply toss out such terms without knowing the full specifics of the problem you're trying to solve, however, is a bit like saying that you know English, French and a little German so sure, by all means - mix them all together in a single paragraph without knowing anything about the target audience! Similarly, you can parallelize a given computation in any number of ways, across any number of different processing elements, but whether that parallelization is actually a win or not is going to be entirely dependent on the nature of the algorithm, its data dependencies, how much computation is expected for each reasonable "work chunk", and whether it can be executed on a GPU with sufficient numeric precision, among many other factors. The more complex the technology you choose, the more those factors matter and the greater the possibility that the resulting code will actually be slower than its single-threaded, single machine counterpart. IPC overhead and data copying can, and frequently do, swamp all of the gains one might realize from trying to naively parallelize something and then add additional overhead on top of that, resulting in a net loss. This is why engineers who can do this kind of work meaningfully and well are in such high demand. :)
Without knowing anything about your calculations, I would move in baby steps. First try a simple multi-processor framework like GCD (which is already built in to OS X and requires no additional dependencies to use) and figure out how to factor your code such that it can effectively use all of the available cores on a single machine. Once you've learned where the wins are (and if there even are any - if multi-threading isn't helping, multi-machine parallelization almost certainly won't either), try setting up several instances of the calculation on several machines with a simple IPC model that allows for distributing the work. Having already factored your algorithm(s) for multiple threads, it should be comparatively straight-forward to further generalize the approach across multiple machines (though it bears noting that the two are NOT the same problem and either way you still want to use all the cores available on any of the given target machines, so the two challenges are both complimentary and orthogonal).
The guys who wrote Bespin (cloud-based canvas-based code editor [and more]) recently spoke about how they re-factored and optimize a portion of the Bespin code because of a misconception that JavaScript was slow. It turned out that when all was said and done, their optimization produced no significant improvements.
I'm sure many of us go out of our way to write "optimized" code based on misconceptions similar to that of the Bespin team.
What are some common performance bottleneck misconceptions developers commonly subscribe to?
In no particular order:
"Ready, Fire, Aim" - thinking you know what needs to be optimized without proving it (i.e. guessing) and then acting on that, and since it doesn't help much, therefore assuming the code must have been optimal to begin with.
"Penny Wise, Pound Foolish" - thinking that optimization is all about compiler optimization, fussing about ++i vs. i++ while mountains of time are being spent needlessly in overblown designs, especially of data structures and databases.
"Swat Flies With a Bazooka" - being so enamored of the fanciest ideas heard in classrooms that they are just used for everything, regardless of scale.
"Fuzzy Thinking about Performance" - throwing around terms like "hotspot" and "bottleneck" and "profiler" and "measure" as if these things were well understood and / or relevant. (I bet I get clobbered for that!) OK, one at a time:
hotspot - What's the definition? I have one: it is a region of physical addresses where the PC register is found a significant fraction of time. It is the kind of thing PC samplers are good at finding. Many performance problems exhibit hotspots, but only in the simplest programs is the problem in the same place as the hotspot is.
bottleneck - A catch-all term used for performance problems, it implies a limited channel constraining how fast work can be accomplished. The unstated assumption is that the work is necessary. In my decades of performance tuning, I have in fact found a few problems like that - very few. Nearly all are of a very different nature. Rather than taking the shortest route from point A to B, little detours are taken, in the form of function calls which take little code, but not little time. Then those detours take further nested detours, sometimes 30 levels deep. The more the detours are nested, the more likely it is that some of them are less than necessary - wasteful, in fact - and it nearly always arises from galloping generality - unquestioning over-indulgence in "abstraction".
profiler - a universal good thing, right? All you gotta do is get a profiler and do some profiling, right? Ever think about how easy it is to fool a profiler into telling you a lot of nothing, when your goal is to find out what you need to fix to get better performance? Somewhere in your call tree, tuck a little file I/O, or a little call to some system routine, or have your evil twin do it without your knowledge. At some point, that will be your problem, and most profilers will miss it completely because the only inefficiency they contemplate is algorithmic inefficiency. Or, not all your routines will be little, and they may not call another routine in a small number of places, so your call graph says there's a link between the two routines, but which call? Or suppose you can figure out that some big percent of time is spent in a path A calls B calls C. You can look at that and think there's not much you can do about it, when if you could also look at the data being passed in those calls, you could see if it's necesssary. Here's a fun project - pick your favorite profiler, and then see how many ways you could fool it. That is, find ways to make the program take longer without the profiler being able to tell what you did, because if you can do it intentionally, you can also do it without intending to.
measure - (i.e. measure time) that's what profilers have done for decades, and they take pride in the accuracy and precision with which they measure. But measure what time? and why with accuracy? Remember, the goal is to precisely locate performance problems, such that you could fruitfully optimize them to gain a speedup. When you get that speedup, it is what it is, regardless of how precisely you estimated it beforehand. If that precision of measurement is bought at the expense of precision of location, then you've just bought apples when what you needed was oranges.
Here's a list of myths about performance.
And this is what happens when one optimizes without a valid profile in hand. All you are doing without a profile is guessing and probably wasting time and money. I could list a bunch of other misconceptions, but many come down to the fact that if the code in question isn't a top resource consumer, it is probably fine as is. Kinda like unrolling a for loop that is doing disk I/O...
If I convert the whole code base over to [Insert xxx latest technology here], it'll be much faster.
Relational databases are slow.
I'm smarter than the optimizer.
This should be optimized.
Java is slow
And, unrelated:
Some people, when confronted with a problem, think "I know, I'll use regular expressions." Now they have two problems.
-jwz
Optimizing the WRONG part of the code (people, use your profiler!).
The optimizer in my compiler is smart, so I don't have to help it.
Java is fast (LOL)
Relational databases are fast (ROTFL LOL LMAO)
"This has to be as fast as possible."
If you don't have a performance problem, you don't need to worry about optimizing performance (beyond just paying attention to using good algorithms).
This misconception also manifests in attempts to optimize performance for every single aspect of your program. I see this most often with people trying to shave every last millisecond off of a low-volume web application's execution time while failing to take into account that network latency will take orders of magnitude longer than their code's execution time, making any reduction in execution time irrelevant anyhow.
My rules of optimization.
Don't optimize
Don't optimize now.
Profile to identify the problem.
Optimize the component that is taking at least 80% of the time.
Find an optimization that is 10 times faster.
My best optimization has been reducing a report from 3 days to 9 minutes. The optimized code was sped up from three days to three minutes.
In my carreer I have met three people who had been tasked with producing a faster sort on VAX than the native sort. They invariably had been able to produce sorts that took only three times longer.
The rules are simple:
Try to use standard library functions first.
Try to use brute-force and ignorance second.
Prove you've got a problem before trying to do any optimization.
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There are plenty of performance questions on this site already, but it occurs to me that almost all are very problem-specific and fairly narrow. And almost all repeat the advice to avoid premature optimization.
Let's assume:
the code already is working correctly
the algorithms chosen are already optimal for the circumstances of the problem
the code has been measured, and the offending routines have been isolated
all attempts to optimize will also be measured to ensure they do not make matters worse
What I am looking for here is strategies and tricks to squeeze out up to the last few percent in a critical algorithm when there is nothing else left to do but whatever it takes.
Ideally, try to make answers language agnostic, and indicate any down-sides to the suggested strategies where applicable.
I'll add a reply with my own initial suggestions, and look forward to whatever else the Stack Overflow community can think of.
OK, you're defining the problem to where it would seem there is not much room for improvement. That is fairly rare, in my experience. I tried to explain this in a Dr. Dobbs article in November 1993, by starting from a conventionally well-designed non-trivial program with no obvious waste and taking it through a series of optimizations until its wall-clock time was reduced from 48 seconds to 1.1 seconds, and the source code size was reduced by a factor of 4. My diagnostic tool was this. The sequence of changes was this:
The first problem found was use of list clusters (now called "iterators" and "container classes") accounting for over half the time. Those were replaced with fairly simple code, bringing the time down to 20 seconds.
Now the largest time-taker is more list-building. As a percentage, it was not so big before, but now it is because the bigger problem was removed. I find a way to speed it up, and the time drops to 17 seconds.
Now it is harder to find obvious culprits, but there are a few smaller ones that I can do something about, and the time drops to 13 sec.
Now I seem to have hit a wall. The samples are telling me exactly what it is doing, but I can't seem to find anything that I can improve. Then I reflect on the basic design of the program, on its transaction-driven structure, and ask if all the list-searching that it is doing is actually mandated by the requirements of the problem.
Then I hit upon a re-design, where the program code is actually generated (via preprocessor macros) from a smaller set of source, and in which the program is not constantly figuring out things that the programmer knows are fairly predictable. In other words, don't "interpret" the sequence of things to do, "compile" it.
That redesign is done, shrinking the source code by a factor of 4, and the time is reduced to 10 seconds.
Now, because it's getting so quick, it's hard to sample, so I give it 10 times as much work to do, but the following times are based on the original workload.
More diagnosis reveals that it is spending time in queue-management. In-lining these reduces the time to 7 seconds.
Now a big time-taker is the diagnostic printing I had been doing. Flush that - 4 seconds.
Now the biggest time-takers are calls to malloc and free. Recycle objects - 2.6 seconds.
Continuing to sample, I still find operations that are not strictly necessary - 1.1 seconds.
Total speedup factor: 43.6
Now no two programs are alike, but in non-toy software I've always seen a progression like this. First you get the easy stuff, and then the more difficult, until you get to a point of diminishing returns. Then the insight you gain may well lead to a redesign, starting a new round of speedups, until you again hit diminishing returns. Now this is the point at which it might make sense to wonder whether ++i or i++ or for(;;) or while(1) are faster: the kinds of questions I see so often on Stack Overflow.
P.S. It may be wondered why I didn't use a profiler. The answer is that almost every one of these "problems" was a function call site, which stack samples pinpoint. Profilers, even today, are just barely coming around to the idea that statements and call instructions are more important to locate, and easier to fix, than whole functions.
I actually built a profiler to do this, but for a real down-and-dirty intimacy with what the code is doing, there's no substitute for getting your fingers right in it. It is not an issue that the number of samples is small, because none of the problems being found are so tiny that they are easily missed.
ADDED: jerryjvl requested some examples. Here is the first problem. It consists of a small number of separate lines of code, together taking over half the time:
/* IF ALL TASKS DONE, SEND ITC_ACKOP, AND DELETE OP */
if (ptop->current_task >= ILST_LENGTH(ptop->tasklist){
. . .
/* FOR EACH OPERATION REQUEST */
for ( ptop = ILST_FIRST(oplist); ptop != NULL; ptop = ILST_NEXT(oplist, ptop)){
. . .
/* GET CURRENT TASK */
ptask = ILST_NTH(ptop->tasklist, ptop->current_task)
These were using the list cluster ILST (similar to a list class). They are implemented in the usual way, with "information hiding" meaning that the users of the class were not supposed to have to care how they were implemented. When these lines were written (out of roughly 800 lines of code) thought was not given to the idea that these could be a "bottleneck" (I hate that word). They are simply the recommended way to do things. It is easy to say in hindsight that these should have been avoided, but in my experience all performance problems are like that. In general, it is good to try to avoid creating performance problems. It is even better to find and fix the ones that are created, even though they "should have been avoided" (in hindsight). I hope that gives a bit of the flavor.
Here is the second problem, in two separate lines:
/* ADD TASK TO TASK LIST */
ILST_APPEND(ptop->tasklist, ptask)
. . .
/* ADD TRANSACTION TO TRANSACTION QUEUE */
ILST_APPEND(trnque, ptrn)
These are building lists by appending items to their ends. (The fix was to collect the items in arrays, and build the lists all at once.) The interesting thing is that these statements only cost (i.e. were on the call stack) 3/48 of the original time, so they were not in fact a big problem at the beginning. However, after removing the first problem, they cost 3/20 of the time and so were now a "bigger fish". In general, that's how it goes.
I might add that this project was distilled from a real project I helped on. In that project, the performance problems were far more dramatic (as were the speedups), such as calling a database-access routine within an inner loop to see if a task was finished.
REFERENCE ADDED:
The source code, both original and redesigned, can be found in www.ddj.com, for 1993, in file 9311.zip, files slug.asc and slug.zip.
EDIT 2011/11/26:
There is now a SourceForge project containing source code in Visual C++ and a blow-by-blow description of how it was tuned. It only goes through the first half of the scenario described above, and it doesn't follow exactly the same sequence, but still gets a 2-3 order of magnitude speedup.
Suggestions:
Pre-compute rather than re-calculate: any loops or repeated calls that contain calculations that have a relatively limited range of inputs, consider making a lookup (array or dictionary) that contains the result of that calculation for all values in the valid range of inputs. Then use a simple lookup inside the algorithm instead.
Down-sides: if few of the pre-computed values are actually used this may make matters worse, also the lookup may take significant memory.
Don't use library methods: most libraries need to be written to operate correctly under a broad range of scenarios, and perform null checks on parameters, etc. By re-implementing a method you may be able to strip out a lot of logic that does not apply in the exact circumstance you are using it.
Down-sides: writing additional code means more surface area for bugs.
Do use library methods: to contradict myself, language libraries get written by people that are a lot smarter than you or me; odds are they did it better and faster. Do not implement it yourself unless you can actually make it faster (i.e.: always measure!)
Cheat: in some cases although an exact calculation may exist for your problem, you may not need 'exact', sometimes an approximation may be 'good enough' and a lot faster in the deal. Ask yourself, does it really matter if the answer is out by 1%? 5%? even 10%?
Down-sides: Well... the answer won't be exact.
When you can't improve the performance any more - see if you can improve the perceived performance instead.
You may not be able to make your fooCalc algorithm faster, but often there are ways to make your application seem more responsive to the user.
A few examples:
anticipating what the user is going
to request and start working on that
before then
displaying results as
they come in, instead of all at once
at the end
Accurate progress meter
These won't make your program faster, but it might make your users happier with the speed you have.
I spend most of my life in just this place. The broad strokes are to run your profiler and get it to record:
Cache misses. Data cache is the #1 source of stalls in most programs. Improve cache hit rate by reorganizing offending data structures to have better locality; pack structures and numerical types down to eliminate wasted bytes (and therefore wasted cache fetches); prefetch data wherever possible to reduce stalls.
Load-hit-stores. Compiler assumptions about pointer aliasing, and cases where data is moved between disconnected register sets via memory, can cause a certain pathological behavior that causes the entire CPU pipeline to clear on a load op. Find places where floats, vectors, and ints are being cast to one another and eliminate them. Use __restrict liberally to promise the compiler about aliasing.
Microcoded operations. Most processors have some operations that cannot be pipelined, but instead run a tiny subroutine stored in ROM. Examples on the PowerPC are integer multiply, divide, and shift-by-variable-amount. The problem is that the entire pipeline stops dead while this operation is executing. Try to eliminate use of these operations or at least break them down into their constituent pipelined ops so you can get the benefit of superscalar dispatch on whatever the rest of your program is doing.
Branch mispredicts. These too empty the pipeline. Find cases where the CPU is spending a lot of time refilling the pipe after a branch, and use branch hinting if available to get it to predict correctly more often. Or better yet, replace branches with conditional-moves wherever possible, especially after floating point operations because their pipe is usually deeper and reading the condition flags after fcmp can cause a stall.
Sequential floating-point ops. Make these SIMD.
And one more thing I like to do:
Set your compiler to output assembly listings and look at what it emits for the hotspot functions in your code. All those clever optimizations that "a good compiler should be able to do for you automatically"? Chances are your actual compiler doesn't do them. I've seen GCC emit truly WTF code.
Throw more hardware at it!
More suggestions:
Avoid I/O: Any I/O (disk, network, ports, etc.) is
always going to be far slower than any code that is
performing calculations, so get rid of any I/O that you do
not strictly need.
Move I/O up-front: Load up all the data you are going
to need for a calculation up-front, so that you do not
have repeated I/O waits within the core of a critical
algorithm (and maybe as a result repeated disk seeks, when
loading all the data in one hit may avoid seeking).
Delay I/O: Do not write out your results until the
calculation is over, store them in a data structure and
then dump that out in one go at the end when the hard work
is done.
Threaded I/O: For those daring enough, combine 'I/O
up-front' or 'Delay I/O' with the actual calculation by
moving the loading into a parallel thread, so that while
you are loading more data you can work on a calculation on
the data you already have, or while you calculate the next
batch of data you can simultaneously write out the results
from the last batch.
Since many of the performance problems involve database issues, I'll give you some specific things to look at when tuning queries and stored procedures.
Avoid cursors in most databases. Avoid looping as well. Most of the time, data access should be set-based, not record by record processing. This includes not reusing a single record stored procedure when you want to insert 1,000,000 records at once.
Never use select *, only return the fields you actually need. This is especially true if there are any joins as the join fields will be repeated and thus cause unnecesary load on both the server and the network.
Avoid the use of correlated subqueries. Use joins (including joins to derived tables where possible) (I know this is true for Microsoft SQL Server, but test the advice when using a differnt backend).
Index, index, index. And get those stats updated if applicable to your database.
Make the query sargable. Meaning avoid things which make it impossible to use the indexes such as using a wildcard in the first character of a like clause or a function in the join or as the left part of a where statement.
Use correct data types. It is faster to do date math on a date field than to have to try to convert a string datatype to a date datatype, then do the calculation.
Never put a loop of any kind into a trigger!
Most databases have a way to check how the query execution will be done. In Microsoft SQL Server this is called an execution plan. Check those first to see where problem areas lie.
Consider how often the query runs as well as how long it takes to run when determining what needs to be optimized. Sometimes you can gain more perfomance from a slight tweak to a query that runs millions of times a day than you can from wiping time off a long_running query that only runs once a month.
Use some sort of profiler tool to find out what is really being sent to and from the database. I can remember one time in the past where we couldn't figure out why the page was so slow to load when the stored procedure was fast and found out through profiling that the webpage was asking for the query many many times instead of once.
The profiler will also help you to find who are blocking who. Some queries that execute quickly while running alone may become really slow due to locks from other queries.
The single most important limiting factor today is the limited memory bandwitdh. Multicores are just making this worse, as the bandwidth is shared betwen cores. Also, the limited chip area devoted to implementing caches is also divided among the cores and threads, worsening this problem even more. Finally, the inter-chip signalling needed to keep the different caches coherent also increase with an increased number of cores. This also adds a penalty.
These are the effects that you need to manage. Sometimes through micro managing your code, but sometimes through careful consideration and refactoring.
A lot of comments already mention cache friendly code. There are at least two distinct flavors of this:
Avoid memory fetch latencies.
Lower memory bus pressure (bandwidth).
The first problem specifically has to do with making your data access patterns more regular, allowing the hardware prefetcher to work efficiently. Avoid dynamic memory allocation which spreads your data objects around in memory. Use linear containers instead of linked lists, hashes and trees.
The second problem has to do with improving data reuse. Alter your algorithms to work on subsets of your data that do fit in available cache, and reuse that data as much as possible while it is still in the cache.
Packing data tighter and making sure you use all data in cache lines in the hot loops, will help avoid these other effects, and allow fitting more useful data in the cache.
What hardware are you running on? Can you use platform-specific optimizations (like vectorization)?
Can you get a better compiler? E.g. switch from GCC to Intel?
Can you make your algorithm run in parallel?
Can you reduce cache misses by reorganizing data?
Can you disable asserts?
Micro-optimize for your compiler and platform. In the style of, "at an if/else, put the most common statement first"
Although I like Mike Dunlavey's answer, in fact it is a great answer indeed with supporting example, I think it could be expressed very simply thus:
Find out what takes the largest amounts of time first, and understand why.
It is the identification process of the time hogs that helps you understand where you must refine your algorithm. This is the only all-encompassing language agnostic answer I can find to a problem that's already supposed to be fully optimised. Also presuming you want to be architecture independent in your quest for speed.
So while the algorithm may be optimised, the implementation of it may not be. The identification allows you to know which part is which: algorithm or implementation. So whichever hogs the time the most is your prime candidate for review. But since you say you want to squeeze the last few % out, you might want to also examine the lesser parts, the parts that you have not examined that closely at first.
Lastly a bit of trial and error with performance figures on different ways to implement the same solution, or potentially different algorithms, can bring insights that help identify time wasters and time savers.
HPH,
asoudmove.
You should probably consider the "Google perspective", i.e. determine how your application can become largely parallelized and concurrent, which will inevitably also mean at some point to look into distributing your application across different machines and networks, so that it can ideally scale almost linearly with the hardware that you throw at it.
On the other hand, the Google folks are also known for throwing lots of manpower and resources at solving some of the issues in projects, tools and infrastructure they are using, such as for example whole program optimization for gcc by having a dedicated team of engineers hacking gcc internals in order to prepare it for Google-typical use case scenarios.
Similarly, profiling an application no longer means to simply profile the program code, but also all its surrounding systems and infrastructure (think networks, switches, server, RAID arrays) in order to identify redundancies and optimization potential from a system's point of view.
Inline routines (eliminate call/return and parameter pushing)
Try eliminating tests/switches with table look ups (if they're faster)
Unroll loops (Duff's device) to the point where they just fit in the CPU cache
Localize memory access so as not to blow your cache
Localize related calculations if the optimizer isn't already doing that
Eliminate loop invariants if the optimizer isn't already doing that
When you get to the point that you're using efficient algorithms its a question of what you need more speed or memory. Use caching to "pay" in memory for more speed or use calculations to reduce the memory footprint.
If possible (and more cost effective) throw hardware at the problem - faster CPU, more memory or HD could solve the problem faster then trying to code it.
Use parallelization if possible - run part of the code on multiple threads.
Use the right tool for the job. some programing languages create more efficient code, using managed code (i.e. Java/.NET) speed up development but native programing languages creates faster running code.
Micro optimize. Only were applicable you can use optimized assembly to speed small pieces of code, using SSE/vector optimizations in the right places can greatly increase performance.
Divide and conquer
If the dataset being processed is too large, loop over chunks of it. If you've done your code right, implementation should be easy. If you have a monolithic program, now you know better.
First of all, as mentioned in several prior answers, learn what bites your performance - is it memory or processor or network or database or something else. Depending on that...
...if it's memory - find one of the books written long time ago by Knuth, one of "The Art of Computer Programming" series. Most likely it's one about sorting and search - if my memory is wrong then you'll have to find out in which he talks about how to deal with slow tape data storage. Mentally transform his memory/tape pair into your pair of cache/main memory (or in pair of L1/L2 cache) respectively. Study all the tricks he describes - if you don's find something that solves your problem, then hire professional computer scientist to conduct a professional research. If your memory issue is by chance with FFT (cache misses at bit-reversed indexes when doing radix-2 butterflies) then don't hire a scientist - instead, manually optimize passes one-by-one until you're either win or get to dead end. You mentioned squeeze out up to the last few percent right? If it's few indeed you'll most likely win.
...if it's processor - switch to assembly language. Study processor specification - what takes ticks, VLIW, SIMD. Function calls are most likely replaceable tick-eaters. Learn loop transformations - pipeline, unroll. Multiplies and divisions might be replaceable / interpolated with bit shifts (multiplies by small integers might be replaceable with additions). Try tricks with shorter data - if you're lucky one instruction with 64 bits might turn out replaceable with two on 32 or even 4 on 16 or 8 on 8 bits go figure. Try also longer data - eg your float calculations might turn out slower than double ones at particular processor. If you have trigonometric stuff, fight it with pre-calculated tables; also keep in mind that sine of small value might be replaced with that value if loss of precision is within allowed limits.
...if it's network - think of compressing data you pass over it. Replace XML transfer with binary. Study protocols. Try UDP instead of TCP if you can somehow handle data loss.
...if it's database, well, go to any database forum and ask for advice. In-memory data-grid, optimizing query plan etc etc etc.
HTH :)
Caching! A cheap way (in programmer effort) to make almost anything faster is to add a caching abstraction layer to any data movement area of your program. Be it I/O or just passing/creation of objects or structures. Often it's easy to add caches to factory classes and reader/writers.
Sometimes the cache will not gain you much, but it's an easy method to just add caching all over and then disable it where it doesn't help. I've often found this to gain huge performance without having to micro-analyse the code.
I think this has already been said in a different way. But when you're dealing with a processor intensive algorithm, you should simplify everything inside the most inner loop at the expense of everything else.
That may seem obvious to some, but it's something I try to focus on regardless of the language I'm working with. If you're dealing with nested loops, for example, and you find an opportunity to take some code down a level, you can in some cases drastically speed up your code. As another example, there are the little things to think about like working with integers instead of floating point variables whenever you can, and using multiplication instead of division whenever you can. Again, these are things that should be considered for your most inner loop.
Sometimes you may find benefit of performing your math operations on an integer inside the inner loop, and then scaling it down to a floating point variable you can work with afterwards. That's an example of sacrificing speed in one section to improve the speed in another, but in some cases the pay off can be well worth it.
I've spent some time working on optimising client/server business systems operating over low-bandwidth and long-latency networks (e.g. satellite, remote, offshore), and been able to achieve some dramatic performance improvements with a fairly repeatable process.
Measure: Start by understanding the network's underlying capacity and topology. Talking to the relevant networking people in the business, and make use of basic tools such as ping and traceroute to establish (at a minimum) the network latency from each client location, during typical operational periods. Next, take accurate time measurements of specific end user functions that display the problematic symptoms. Record all of these measurements, along with their locations, dates and times. Consider building end-user "network performance testing" functionality into your client application, allowing your power users to participate in the process of improvement; empowering them like this can have a huge psychological impact when you're dealing with users frustrated by a poorly performing system.
Analyze: Using any and all logging methods available to establish exactly what data is being transmitted and received during the execution of the affected operations. Ideally, your application can capture data transmitted and received by both the client and the server. If these include timestamps as well, even better. If sufficient logging isn't available (e.g. closed system, or inability to deploy modifications into a production environment), use a network sniffer and make sure you really understand what's going on at the network level.
Cache: Look for cases where static or infrequently changed data is being transmitted repetitively and consider an appropriate caching strategy. Typical examples include "pick list" values or other "reference entities", which can be surprisingly large in some business applications. In many cases, users can accept that they must restart or refresh the application to update infrequently updated data, especially if it can shave significant time from the display of commonly used user interface elements. Make sure you understand the real behaviour of the caching elements already deployed - many common caching methods (e.g. HTTP ETag) still require a network round-trip to ensure consistency, and where network latency is expensive, you may be able to avoid it altogether with a different caching approach.
Parallelise: Look for sequential transactions that don't logically need to be issued strictly sequentially, and rework the system to issue them in parallel. I dealt with one case where an end-to-end request had an inherent network delay of ~2s, which was not a problem for a single transaction, but when 6 sequential 2s round trips were required before the user regained control of the client application, it became a huge source of frustration. Discovering that these transactions were in fact independent allowed them to be executed in parallel, reducing the end-user delay to very close to the cost of a single round trip.
Combine: Where sequential requests must be executed sequentially, look for opportunities to combine them into a single more comprehensive request. Typical examples include creation of new entities, followed by requests to relate those entities to other existing entities.
Compress: Look for opportunities to leverage compression of the payload, either by replacing a textual form with a binary one, or using actual compression technology. Many modern (i.e. within a decade) technology stacks support this almost transparently, so make sure it's configured. I have often been surprised by the significant impact of compression where it seemed clear that the problem was fundamentally latency rather than bandwidth, discovering after the fact that it allowed the transaction to fit within a single packet or otherwise avoid packet loss and therefore have an outsize impact on performance.
Repeat: Go back to the beginning and re-measure your operations (at the same locations and times) with the improvements in place, record and report your results. As with all optimisation, some problems may have been solved exposing others that now dominate.
In the steps above, I focus on the application related optimisation process, but of course you must ensure the underlying network itself is configured in the most efficient manner to support your application too. Engage the networking specialists in the business and determine if they're able to apply capacity improvements, QoS, network compression, or other techniques to address the problem. Usually, they will not understand your application's needs, so it's important that you're equipped (after the Analyse step) to discuss it with them, and also to make the business case for any costs you're going to be asking them to incur. I've encountered cases where erroneous network configuration caused the applications data to be transmitted over a slow satellite link rather than an overland link, simply because it was using a TCP port that was not "well known" by the networking specialists; obviously rectifying a problem like this can have a dramatic impact on performance, with no software code or configuration changes necessary at all.
Very difficult to give a generic answer to this question. It really depends on your problem domain and technical implementation. A general technique that is fairly language neutral: Identify code hotspots that cannot be eliminated, and hand-optimize assembler code.
Last few % is a very CPU and application dependent thing....
cache architectures differ, some chips have on-chip RAM
you can map directly, ARM's (sometimes) have a vector
unit, SH4's a useful matrix opcode. Is there a GPU -
maybe a shader is the way to go. TMS320's are very
sensitive to branches within loops (so separate loops and
move conditions outside if possible).
The list goes on.... But these sorts of things really are
the last resort...
Build for x86, and run Valgrind/Cachegrind against the code
for proper performance profiling. Or Texas Instruments'
CCStudio has a sweet profiler. Then you'll really know where
to focus...
Not nearly as in depth or complex as previous answers, but here goes:
(these are more beginner/intermediate level)
obvious: dry
run loops backwards so you're always comparing to 0 rather than a variable
use bitwise operators whenever you can
break repetitive code into modules/functions
cache objects
local variables have slight performance advantage
limit string manipulation as much as possible
Did you know that a CAT6 cable is capable of 10x better shielding off external inteferences than a default Cat5e UTP cable?
For any non-offline projects, while having best software and best hardware, if your throughoutput is weak, then that thin line is going to squeeze data and give you delays, albeit in milliseconds...
Also the maximum throughput is higher on CAT6 cables because there is a higher chance that you will actually receive a cable whose strands exist of cupper cores, instead of CCA, Cupper Cladded Aluminium, which is often fount in all your standard CAT5e cables.
I if you are facing lost packets, packet drops, then an increase in throughput reliability for 24/7 operation can make the difference that you may be looking for.
For those who seek the ultimate in home/office connection reliability, (and are willing to say NO to this years fastfood restaurants, at the end of the year you can there you can) gift yourself the pinnacle of LAN connectivity in the form of CAT7 cable from a reputable brand.
Impossible to say. It depends on what the code looks like. If we can assume that the code already exists, then we can simply look at it and figure out from that, how to optimize it.
Better cache locality, loop unrolling, Try to eliminate long dependency chains, to get better instruction-level parallelism. Prefer conditional moves over branches when possible. Exploit SIMD instructions when possible.
Understand what your code is doing, and understand the hardware it's running on. Then it becomes fairly simple to determine what you need to do to improve performance of your code. That's really the only truly general piece of advice I can think of.
Well, that, and "Show the code on SO and ask for optimization advice for that specific piece of code".
If better hardware is an option then definitely go for that. Otherwise
Check you are using the best compiler and linker options.
If hotspot routine in different library to frequent caller, consider moving or cloning it to the callers module. Eliminates some of the call overhead and may improve cache hits (cf how AIX links strcpy() statically into separately linked shared objects). This could of course decrease cache hits also, which is why one measure.
See if there is any possibility of using a specialized version of the hotspot routine. Downside is more than one version to maintain.
Look at the assembler. If you think it could be better, consider why the compiler did not figure this out, and how you could help the compiler.
Consider: are you really using the best algorithm? Is it the best algorithm for your input size?
The google way is one option "Cache it.. Whenever possible don't touch the disk"
Here are some quick and dirty optimization techniques I use. I consider this to be a 'first pass' optimization.
Learn where the time is spent Find out exactly what is taking the time. Is it file IO? Is it CPU time? Is it the network? Is it the Database? It's useless to optimize for IO if that's not the bottleneck.
Know Your Environment Knowing where to optimize typically depends on the development environment. In VB6, for example, passing by reference is slower than passing by value, but in C and C++, by reference is vastly faster. In C, it is reasonable to try something and do something different if a return code indicates a failure, while in Dot Net, catching exceptions are much slower than checking for a valid condition before attempting.
Indexes Build indexes on frequently queried database fields. You can almost always trade space for speed.
Avoid lookups Inside of the loop to be optimized, I avoid having to do any lookups. Find the offset and/or index outside of the loop and reuse the data inside.
Minimize IO try to design in a manner that reduces the number of times you have to read or write especially over a networked connection
Reduce Abstractions The more layers of abstraction the code has to work through, the slower it is. Inside the critical loop, reduce abstractions (e.g. reveal lower-level methods that avoid extra code)
Spawn Threads for projects with a user interface, spawning a new thread to preform slower tasks makes the application feel more responsive, although isn't.
Pre-process You can generally trade space for speed. If there are calculations or other intense operations, see if you can precompute some of the information before you're in the critical loop.
If you have a lot of highly parallel floating point math-especially single-precision-try offloading it to a graphics processor (if one is present) using OpenCL or (for NVidia chips) CUDA. GPUs have immense floating point computing power in their shaders, which is much greater than that of a CPU.
Adding this answer since I didnt see it included in all the others.
Minimize implicit conversion between types and sign:
This applies to C/C++ at least, Even if you already think you're free of conversions - sometimes its good to test adding compiler warnings around functions that require performance, especially watch-out for conversions within loops.
GCC spesific: You can test this by adding some verbose pragmas around your code,
#ifdef __GNUC__
# pragma GCC diagnostic push
# pragma GCC diagnostic error "-Wsign-conversion"
# pragma GCC diagnostic error "-Wdouble-promotion"
# pragma GCC diagnostic error "-Wsign-compare"
# pragma GCC diagnostic error "-Wconversion"
#endif
/* your code */
#ifdef __GNUC__
# pragma GCC diagnostic pop
#endif
I've seen cases where you can get a few percent speedup by reducing conversions raised by warnings like this.
In some cases I have a header with strict warnings that I keep included to prevent accidental conversions, however this is a trade-off since you may end up adding a lot of casts to quiet intentional conversions which may just make the code more cluttered for minimal gains.
Sometimes changing the layout of your data can help. In C, you might switch from an array or structures to a structure of arrays, or vice versa.
Tweak the OS and framework.
It may sound an overkill but think about it like this: Operating Systems and Frameworks are designed to do many things. Your application only does very specific things. If you could get the OS do to exactly what your application needs and have your application understand how the the framework (php,.net,java) works, you could get much better out of your hardware.
Facebook, for example, changed some kernel level thingys in Linux, changed how memcached works (for example they wrote a memcached proxy, and used udp instead of tcp).
Another example for this is Window2008. Win2K8 has a version were you can install just the basic OS needed to run X applicaions (e.g. Web-Apps, Server Apps). This reduces much of the overhead that the OS have on running processes and gives you better performance.
Of course, you should always throw in more hardware as the first step...
It's a tricky question I was asked the other day... We're working on a pretty complex telephony (SIP) application with mixed C++ and PHP code with MySQL databases and several open source components.
A telecom engineer asked us to estimate the performance of the application (which is not ready yet). He went like 'well, you know how many packets can pass through the Linux kernel per second, plus you might know how quick your app is, so tell me how many calls will pass through your stuff per second'.
Seems nonsense to me, as there are a million scenarios that might happen (well, literally...)
However... is there a way to estimate application performance (knowing the hardware it will run on, being able to run standard benchmarks on it, etc) before actual testing?
You certainly can bound the problem with upper (max throughput) limits. There is nothing nonsense about that. In fact, not knowing that stuff indicates a pretty haphazard approach to a problem - especially in the telephony world.
You can work through the problem yourself - what is the minimum "work" you have to accomplish for a transaction or whatever unit of task you have in your app?
Some messages to and from, some processing and a database hit for example? Getting information on the individual pieces will give you an idea of the fastest possible throughput. If you load up the system and see significantly lower performance then you can take time to figure out where you are possibly losing throughput with inefficient algorithms, etc.
EDIT
To do this exercise you need to know all the steps your app does for each use case. Then you can identify the max throughput for each use case. You should definitely know this stuff prior to release and going live.
I'm ignoring the worst case analysis as that - as you point out - is quite a bit harder.
See Capacity Planning for Web Performance: Metrics, Models, and Methods. There are also some tools that can do this sort of discrete event simulation:
Hyperformix
SimPy
WikiPedia list of simulation tools
This stuff ain't easy, and the commercial tools will cost ya. The Capacity Planning book comes with a CD with lots of Excel workbook templates and examples of models that can jump start you.
Good luck :)
If you really have to answer this you could say something like this:
"I don't know off the top of my head. I am will to estimate this for you but it will take time. Obviously the accuracy of my answer depends upon how much effort (I.E. time) I put into calculating my estimate. How much time should I put into calculating my estimate?"
Put the burden back on them. If they really want an accurate answer, they're going to have to let you build at least some test applications that can simulate the actual environment.
You can spike to measure performance. Your whole system may not be working yet, but you know how the parts are intended to fit together. You can whip something up in a few hours that does the same kind of work as the final app will, across all the layers, and use it to measure performance of your design.
Remember: prototypes are broad, spikes are deep.
You should do the estimate. An estimate won't give you the right answer. It will however make you to think about the problem. Right now it sounds like your coding and hoping that everything will be OK. Or you are in panic mode and feel you don't have time for estimates.
Spend some time thinking about it. Analyse the important use cases. Think about the memory you may need; think about database access; think about network access (local and remote). These will effect the performance of your system. Get the whole team together to do this.
Regularly measure your system's performance during development for these important use cases. Mock up components/other systems if you have to. Analyse the results. How do these compare to your estimate. Maybe components are memory/database/network bound. Maybe you need more memory; less database access; simpler queries; caching. You don't have to make these changes straight away. However you do know how your system operates and what you need to do.
Result: Fewer nasty surprises at system test. Less panic as the release date looms.
You can definitely do capacity planning in advance, but the quality of the estimate will depend on the quality of the data available.
The best estimate is to build the system in test, run simulated workloads, then predict capacity as a function of performance requirements and workload. These 3 form a prediction space - given 2 of the 3, you can predict the third:
Given performance requirements and capacity (i.e. hardware) you can calculate the workload you can handle.
Given performance requirements and workload, you can calculate the capacity (i.e. hardware) that you need.
Given Workload and capacity, you can predict your expected performance.
This is true in some domains, but unless you are an expert in that domain then you don't have any idea. For example I write code to controlling industrial robots. The speed is limited by the robot motion, not by the execution speed of the code. Knowing how fast the robot is and how far it has to go, we can make fairly good estimates of "speed". I'd have no idea how to estimate time for your application.
Let's just assume for now that you have narrowed down where the typical bottlenecks in your app are. For all you know, it might be the batch process you run to reindex your tables; it could be the SQL queries that runs over your effective-dated trees; it could be the XML marshalling of a few hundred composite objects. In other words, you might have something like this:
public Result takeAnAnnoyingLongTime(Input in) {
// impl of above
}
Unfortunately, even after you've identified your bottleneck, all you can do is chip away at it. No simple solution is available.
How do you measure the performance of your bottleneck so that you know your fixes are headed in the right direction?
Two points:
Beware of the infamous "optimizing the idle loop" problem. (E.g. see the optimization story under the heading "Porsche-in-the-parking-lot".) That is, just because a routine is taking a significant amount of time (as shown by your profiling), don't assume that it's responsible for slow performance as perceived by the user.
The biggest performance gains often come not from that clever tweak or optimization to the implementation of the algorithm, but from realising that there's a better algorithm altogether. Some improvements are relatively obvious, while others require more detailed analysis of the algorithms, and possibly a major change to the data structures involved. This may include trading off processor time for I/O time, in which case you need to make sure that you're not optimizing only one of those measures.
Bringing it back to the question asked, make sure that whatever you're measuring represents what the user actually experiences, otherwise your efforts could be a complete waste of time.
Profile it
Find the top line in the profiler, attempt to make it faster.
Profile it
If it worked, go to 1. If it didn't work, go to 2.
I'd measure them using the same tools / methods that allowed me to find them in the first place.
Namely, sticking timing and logging calls all over the place. If the numbers start going down, then you just might be doing the right thing.
As mentioned in this msdn column, performance tuning is compared to the job of painting Golden Gate Bridge: once you finish painting the entire thing, it's time to go back to the beginning and start again.
This is not a hard problem. The first thing you need to understand is that measuring performance is not how you find performance problems. Knowing how slow something is doesn't help you find out why. You need a diagnostic tool, and a good one. I've had a lot of experience doing this, and this is the best method. It is not automatic, but it runs rings around most profilers.
It's an interesting question. I don't think anyone knows the answer. I believe that significant part of the problem is that for more complicated programs, no one can predict their complexity. Therefore, even if you have profiler results, it's very complicated to interpret it in terms of changes that should be made to the program, because you have no theoretical basis for what the optimal solution is.
I think this is a reason why we have so bloated software. We optimize only so that quite simple cases would work on our fast machines. But once you put such pieces together into a large system, or you use order of magnitude larger input, wrong algorithms used (which were until then invisible both theoretically and practically) will start showing their true complexity.
Example: You create a string class, which handles Unicode. You use it somewhere like computer-generated XML processing where it really doesn't matter. But Unicode processing is in there, taking part of the resources. By itself, the string class can be very fast, but call it million times, and the program will be slow.
I believe that most of the current software bloat is of this nature. There is a way to reduce it, but it contradicts OOP. There is an interesting book There is an interesting book about various techniques, it's memory oriented but most of them could be reverted to get more speed.
I'd identify two things:
1) what complexity is it? The easiest way is to graph time-taken verses size of input.
2) how is it bound? Is it memory, or disk, or IPC with other processes or machines, or..
Now point (2) is the easier to tackle and explain: Lots of things go faster if you have more RAM or a faster machine or faster disks or move over to gig ethernet or such. If you identify your pain, you can put some money into hardware to make it tolerable.