I am a mere astronomer, so this is quite probably an obvious question.
Having no experience with parallel computing and hardly any with optimizing performance in general: my machine has four cores. If I ignorantly run my code, will all four be utilized automatically?
Chances are your code is not executed on all cores but just one. It depends if you code using a specific platform/library that already abstracts thread management.
Depending on the language, you want to have a look at specific libraries. But thread programming is a general subject you have to further explore before choosing any.
Related
This is more a conceptual/high level question rather than a language specific one.
In terms of writing software for high performance applications that will use multiple threads, would it be advisable to manually check for and only allocate threads to P cores, or rather just simply allocate threads as they were done before Alder Lake, and let the OS scheduler decide where to put them?
To be more specific, my program will be a computer game with separate computationally expensive CPU threads for AI, pathfinding, etc. Ideally I don't want these threads on E cores but I'm wondering if I should be leaving this sort of thing up to the OS to decide instead of ensuring it manually.
This is probably not a good question for SO, and might fit better on https://softwareengineering.stackexchange.com, but here goes with a conceptual/high-level answer anyways.
In terms of writing software for high performance applications you will get best performance by writing platform-dependent code, that is by writing programs which are informed by, and take advantage of, the particular features of the hardware+o/s+runtime on which the programs are to execute.
The costs of this approach are that the code will, by definition, be less-than-optimal-performancewise on any other platform; and that writing codes to squeeze out every last drop of performance for a particular problem can be quite difficult and time consuming.
Personally (so this might be an opinion, which is something SO doesn't like) I would first write the platform-neutral version of the code and test it. Only when I was convinced that I couldn't achieve necessary performance (or other) goals would I roll up my sleeves and develop that first version into a platform-dependent version. (Well, I might do this extra work for fun, but you catch my drift).
Later, if you want to move the program to another platform you already have the platform-neutral version to start with.
The vast majority of CPUs coming out nowadays contain multiple cores which can operate at the same time - in parallel.
I'm just wondering, from the point of executing a program as quickly as possible using all available CPU cores, does a programmer need to take into consideration that the software being developed will be running on a multi-core CPU? For instance, would the software being developed have to be manually configured to assign different tasks to each CPU core? Or does the OS/CPU automatically identify and choose which parts of a program can run - in parallel - on different cores?
Apologies if this may seem like a simple or silly question. I'm completely new to the topic of parallel programming and I've come across some conflicting information early on in my research - some sources state that the programmer must manually configure their software in order to utilise more than one CPU core (the more believable option in my opinion) - and other sources state that the OS/CPU automatically identifies and chooses which tasks can be run in parallel on different CPU cores (the less believable option in my opinion due to the complexity involved in automatically identifying this).
Just in case different Operating Systems, CPUs or Programming Languages perform differently in a parallel computing or multi-core environment - I will be using Windows 7 as my OS, an Intel Dual Core i7 Processor, and OpenCL as the programming language.
Any help is much appreciated.
In practice this occurs semi-automatically.
More detailed answer will depend on your application nature, preferred programming model and target architecture.
More explanation:
In order to exploit multicore hardware efficiently (in your case, keeping as much cores busy as possible) you first of all 1) need to "parallelize" algorithm itself - make it "concurrent", 2) use one of multi-threading (most often) or multi-process (rare case) parallel programming APIs, like for example "OpenMP", "Intel TBB", "OpenCL", "Posix Threads" or (for multi-process) "MPI" in order to efficiently and often automatically assign different "pieces" of your concurrent program to different threads (or, rare case, processes).
One of the simplest possible examples of such kind of parallel programming (using OpenMP) is given here.
Now, you've told that you are using OpenCL as a programming model for CPU. In certain cases, when you use vendor-provided OpenCL implementations (like Intel OpenCL) you could semi-automatically assign OpenCL kernel to be executed by various threads using "NDRange" and other OpenCL concepts, like explained here for Intel Xeon Phi co-processor (not exactly CPU-programming, but similar idea) or here (more general, but more advanced article).
However, using OpenCL as a general-purpose multi-threading programming API for CPU - is definitely not the simplest approach; and it is not always optimal in terms of final performance. There are certain application types, where OpenCL makes some little sense for general-purpose CPU multi-threading programming, but again it very much depends on your algorithm nature and target architecture..
There is one very obsolete, but still reasonable post about OpenCL vs. OpenMP/TBB on stackoverflow. This is obsolete in sense that OpenMP 4.0 now also provides solid capabilities to do Threading*+SIMD* programming (which will make you interested in some future if you explore given topic in more details). That's why I would tell that OpenMP seems to be number-one choice nowadays, bug TBB, MPI or OpenCL might also be appropriate in certain cases.
I have a large scientific computing task that parallelizes very well with SMP, but at too fine grained a level to be easily parallelized via explicit message passing. I'd like to parallelize it across address spaces and physical machines. Is it feasible to create a scheduler that would parallelize already multithreaded code across multiple physical computers under the following conditions:
The code is already multithreaded and can scale pretty well on SMP configurations.
The fact that not all of the threads are running in the same address space or on the same physical machine must be transparent to the program, even if this comes at a significant performance penalty in some use cases.
You may assume that all of the physical machines involved are running operating systems and CPU architectures that are binary compatible.
Things like locks and atomic operations may be slow (having network latency to deal with and all) but must "just work".
Edits:
I only care about throughput, not latency.
I'm using the D programming language, and I'm almost sure there's no canned solution. I'm more interested in whether this is feasible in principle than in a particular canned solution.
My first thought is to use Apache Hadoop. It provides distributed storage and distributed computing. You can synchronize across processes by using files as locks.
It sounds like you want something like SCRAMNet, although that requires custom hardware. I don't know if there is a software-only solution. Also, it's likely that even if you got it working, you'd find your networked version was actually running slower than when it was previously on a single machine. You may just have to bite the bullet and re-design your app.
Since your point 2 suggests that you can live with some performance degradation you might want to consider a hybrid approach: SMP within individual machines, message-passing between machines. I'm not familiar with D so can offer no specific advice. Further I've seen mixed reviews of the hybrid approach for OpenMP+MPI, but it might suit you and your application.
EDIT: You might want to Google around for 'partitioned global address space' which seems to describe your desired approach quite accurately. As before, I have no advice on using D for this.
I want to write a code converter that takes an OpenMP based parallel program and runs it on a cluster.
How do I go about this problem? What libraries do I use? How do I set up a small cluster for this?
I'm finding it extremely hard to find good material about cluster computing on the internet.
EDIT: If it's impossible then how does Intel do it? The Intel compiler seems to do exactly what I want to. I don't have any specific application that I would like to run. I want to write the "converter/compiler", not the application. I understand that shared memory is different from distributed memory, but there has to be a way to sync memory, if not for all cases, then for some specific cases, even if it means that application is written with custom constructs.
Intel has an implementation of OpenMP that works with their C++ and Fortran compilers for x86 64-bit clusters. You can get a 30-day eval version of these compilers for free. Other than that, Zifre is mostly right. If you are concerned with scalability, bite the bullet and write your parallel program in another programming model (MPI, CUDA, Cilk, ...) which is designed with distributed systems in mind. If you provide a little more information about your application, we may be able to provide more useful guidance on that front.
It seems to me that this is not a good idea.
The basic idea behind OpenMP is data-shared parallel execution. It works well, when accessing shared data costs you nothing. Every thread can access a variable in shared cache or RAM.
The cluster computations exploit message-passing, because computers in cluster have distributed memory. When one process needs data from another one then you should manage data passing over the network. It is time-consuming operation.
So, if you want to write such compiler, you should implement data broadcasting operations (e.g. MPI_Bcast from MPI) for each data access in OpenMP. This will kill parallel performance at all.
This is simply not possible. You have to structure your code in a completely different way to get it to work on a cluster (programming multiple machines is very different from programming one machine).
There is no magic pixie dust to do this.
On the other hand, if you write your program with clusters in mind, it is possible to run it on a single machine (although it will obviously be slower).
SCORE/SCASH and Omni OpenMP compiler
With the recent buzz on multicore programming is anyone exploring the possibilities of using MPI ?
I've used MPI extensively on large clusters with multi-core nodes. I'm not sure if it's the right thing for a single multi-core box, but if you anticipate that your code may one day scale larger than a single chip, you might consider implementing it in MPI. Right now, nothing scales larger than MPI. I'm not sure where the posters who mention unacceptable overheads are coming from, but I've tried to give an overview of the relevant tradeoffs below. Read on for more.
MPI is the de-facto standard for large-scale scientific computation and it's in wide use on multicore machines already. It is very fast. Take a look at the most recent Top 500 list. The top machines on that list have, in some cases, hundreds of thousands of processors, with multi-socket dual- and quad-core nodes. Many of these machines have very fast custom networks (Torus, Mesh, Tree, etc) and optimized MPI implementations that are aware of the hardware.
If you want to use MPI with a single-chip multi-core machine, it will work fine. In fact, recent versions of Mac OS X come with OpenMPI pre-installed, and you can download an install OpenMPI pretty painlessly on an ordinary multi-core Linux machine. OpenMPI is in use at Los Alamos on most of their systems. Livermore uses mvapich on their Linux clusters. What you should keep in mind before diving in is that MPI was designed for solving large-scale scientific problems on distributed-memory systems. The multi-core boxes you are dealing with probably have shared memory.
OpenMPI and other implementations use shared memory for local message passing by default, so you don't have to worry about network overhead when you're passing messages to local processes. It's pretty transparent, and I'm not sure where other posters are getting their concerns about high overhead. The caveat is that MPI is not the easiest thing you could use to get parallelism on a single multi-core box. In MPI, all the message passing is explicit. It has been called the "assembly language" of parallel programming for this reason. Explicit communication between processes isn't easy if you're not an experienced HPC person, and there are other paradigms more suited for shared memory (UPC, OpenMP, and nice languages like Erlang to name a few) that you might try first.
My advice is to go with MPI if you anticipate writing a parallel application that may need more than a single machine to solve. You'll be able to test and run fine with a regular multi-core box, and migrating to a cluster will be pretty painless once you get it working there. If you are writing an application that will only ever need a single machine, try something else. There are easier ways to exploit that kind of parallelism.
Finally, if you are feeling really adventurous, try MPI in conjunction with threads, OpenMP, or some other local shared-memory paradigm. You can use MPI for the distributed message passing and something else for on-node parallelism. This is where big machines are going; future machines with hundreds of thousands of processors or more are expected to have MPI implementations that scale to all nodes but not all cores, and HPC people will be forced to build hybrid applications. This isn't for the faint of heart, and there's a lot of work to be done before there's an accepted paradigm in this space.
I would have to agree with tgamblin. You'll probably have to roll your sleeves up and really dig into the code to use MPI, explicitly handling the organization of the message-passing yourself. If this is the sort of thing you like or don't mind doing, I would expect that MPI would work just as well on multicore machines as it would on a distributed cluster.
Speaking from personal experience... I coded up some C code in graduate school to do some large scale modeling of electrophysiologic models on a cluster where each node was itself a multicore machine. Therefore, there were a couple of different parallel methods I thought of to tackle the problem.
1) I could use MPI alone, treating every processor as it's own "node" even though some of them are grouped together on the same machine.
2) I could use MPI to handle data moving between multicore nodes, and then use threading (POSIX threads) within each multicore machine, where processors share memory.
For the specific mathematical problem I was working on, I tested two formulations first on a single multicore machine: one using MPI and one using POSIX threads. As it turned out, the MPI implementation was much more efficient, giving a speed-up of close to 2 for a dual-core machine as opposed to 1.3-1.4 for the threaded implementation. For the MPI code, I was able to organize operations so that processors were rarely idle, staying busy while messages were passed between them and masking much of the delay from transferring data. With the threaded code, I ended up with a lot of mutex bottlenecks that forced threads to often sit and wait while other threads completed their computations. Keeping the computational load balanced between threads didn't seem to help this fact.
This may have been specific to just the models I was working on, and the effectiveness of threading vs. MPI would likely vary greatly for other types of parallel problems. Nevertheless, I would disagree that MPI has an unwieldy overhead.
No, in my opinion it is unsuitable for most processing you would do on a multicore system. The overhead is too high, the objects you pass around must be deeply cloned, and passing large objects graphs around to then run a very small computation is very inefficient. It is really meant for sharing data between separate processes, most often running in separate memory spaces, and most often running long computations.
A multicore processor is a shared memory machine, so there are much more efficient ways to do parallel processing, that do not involve copying objects and where most of the threads run for a very small time. For example, think of a multithreaded Quicksort. The overhead of allocating memory and copying the data to a thread before it can be partioned will be much slower with MPI and an unlimited number of processors than Quicksort running on a single processor.
As an example, in Java, I would use a BlockingQueue (a shared memory construct), to pass object references between threads, with very little overhead.
Not that it does not have its place, see for example the Google search cluster that uses message passing. But it's probably not the problem you are trying to solve.
MPI is not inefficient. You need to break the problem down into chunks and pass the chunks around and reorganize when the result is finished per chunk. No one in the right mind would pass around the whole object via MPI when only a portion of the problem is being worked on per thread. Its not the inefficiency of the interface or design pattern thats the inefficiency of the programmers knowledge of how to break up a problem.
When you use a locking mechanism the overhead on the mutex does not scale well. this is due to the fact that the underlining runqueue does not know when you are going to lock the thread next. You will perform more kernel level thrashing using mutex's than a message passing design pattern.
MPI has a very large amount of overhead, primarily to handle inter-process communication and heterogeneous systems. I've used it in cases where a small amount of data is being passed around, and where the ratio of computation to data is large.
This is not the typical usage scenario for most consumer or business tasks, and in any case, as a previous reply mentioned, on a shared memory architecture like a multicore machine, there are vastly faster ways to handle it, such as memory pointers.
If you had some sort of problem with the properties describe above, and you want to be able to spread the job around to other machines, which must be on the same highspeed network as yourself, then maybe MPI could make sense. I have a hard time imagining such a scenario though.
I personally have taken up Erlang( and i like to so far). The messages based approach seem to fit most of the problem and i think that is going to be one of the key item for multi core programming. I never knew about the overhead of MPI and thanks for pointing it out
You have to decide if you want low level threading or high level threading. If you want low level then use pThread. You have to be careful that you don't introduce race conditions and make threading performance work against you.
I have used some OSS packages for (C and C++) that are scalable and optimize the task scheduling. TBB (threading building blocks) and Cilk Plus are good and easy to code and get applications of the ground. I also believe they are flexible enough integrate other thread technologies into it at a later point if needed (OpenMP etc.)
www.threadingbuildingblocks.org
www.cilkplus.org