I want to compare the performance of single-core CPU and multi-core CPU.
I wrote a program and let it iterate 1000 times on a single-core CPU to see the running time. In the multi-core case, I used OpenCL to launch a kernel that where the code is same as that inside the iteration in the first case.
Considered multi-core could run 8 concurrent threads, theoretically, the running time of multi-core case should be above T(single-core)/8.
But the results is that the T(multi-core) is almost 1/20 of T(single-core).
I wonder why this happen? Did OpenCL compiler do some optimization for multi-core CPU ?
If your single core code was scalar, chances are the opencl runtime used sse or avx and get an extra multiplier.
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I implemented a multithreaded program for matrix multiplication and serial program for same I observed that on my laptop multithreaded program giving speedup up to 4 compared serial one but. when I ran the same program on another computer it was giving max speedup of 2 so what is the reason behind it does CPU cores affect speedup and can dual-core CPU never give speedup more than 2?
I have 16000 jobs to perform.
Each job is independent. There is no shared memory, no interprocess communication, no lock or mutex.
I am on ubuntu 16.06. c++11. Intel® Core™ i7-8550U CPU # 1.80GHz × 8
I use std::async to split jobs between cores.
If I split the jobs into 8 (2000 per core), computation time is 145.
If I split the jobs into 4 (4000 per core), computation time is 60.
Output after reduce is the same in both case.
If I monitor the CPU during computation (just using htop), things happen as expected (8 cores are used at 100% in first case, only 4 cores are used 100% in second case).
I am very confused why 4 cores would process much faster than 8.
The i7-8550U has 4 cores and 8 threads.
What is the difference? Quoting How-To Geek:
Hyper-threading was Intel’s first attempt to bring parallel
computation to consumer PCs. It debuted on desktop CPUs with the
Pentium 4 HT back in 2002. The Pentium 4’s of the day featured just a
single CPU core, so it could really only perform one task at a
time—even if it was able to switch between tasks quickly enough that
it seemed like multitasking. Hyper-threading attempted to make up for
that.
A single physical CPU core with hyper-threading appears as two logical
CPUs to an operating system. The CPU is still a single CPU, so it’s a
little bit of a cheat. While the operating system sees two CPUs for
each core, the actual CPU hardware only has a single set of execution
resources for each core. The CPU pretends it has more cores than it
does, and it uses its own logic to speed up program execution. In
other words, the operating system is tricked into seeing two CPUs for
each actual CPU core.
Hyper-threading allows the two logical CPU cores to share physical
execution resources. This can speed things up somewhat—if one virtual
CPU is stalled and waiting, the other virtual CPU can borrow its
execution resources. Hyper-threading can help speed your system up,
but it’s nowhere near as good as having actual additional cores.
By splitting the jobs to more cores than available - you are paying a big penalty.
I need some help understanding the concept of cores on a GPU vs. cores in a CPU for the purpose of doing parallel calculations.
When it comes to cores in a CPU, it seems pretty simple. I have a super intensive "for" loop that iterates four times. I have four cores in my Intel i5 2.26GHz CPU. I give one loop to each core. Each of the four loops is independent of the other. Boom - I now have four threads created and 100% CPU usage (instead of 25% CPU usage with only one core). My "for" loop now runs almost four times faster than it would have if I did not parallelize it. By the way, for the "for" loop, I was using the auto-parallelization available on Microsoft Visual Studio 2012, as in this online example:(http://msdn.microsoft.com/en-us/library/hh872235.aspx).
In contrast, I don't even know the number of cores in my laptop's GPU (Intel Graphics Media Accelerator HD, or Intel HD Graphics, with 1696MB shared memory) that I can use for parallel calculations. I don't even know a valid way of comparing the GPU to the CPU. When I see "12#500MHz" next to my graphics card description, I wonder if that means the graphics card has 12 cores for parallelization that can work kinda like the 4 cores in a CPU, except that the GPU cores run at 500MHz [slow] instead of 2.26GHz [fast]? Is there a GPU usage comparable to the CPU usage in Windows task manager? I'm an utter novice trying to use the C++ library in visual studio 2012, if that makes any difference. When I write the actual GPU software, the parallelization code looks like this:(http://msdn.microsoft.com/en-us/library/hh265137.aspx).
So, would you please fill some of the gaps or mistakes in my knowledge or help me compare the two? I don't need a super complicated answer, something as simple as "You can't compare a CPU core with a GPU core because of blankity blank" or "a GPU core isn't really a core like a CPU core is" would be very much appreciated.
First, the OS initiate more cores only if you ask for them in your code. Try using OpenMP or Win32 threads to achieve parallelism on your i5.
Second, the CPU clocking is more than GPU clocking. If the clocking of GPU is same as CPU, you can use it as a stove to cook. The cores in the GPU are more than CPU. There is a difference between a thread and core.
Third, I recommend you to read specifications and reference manuals for your CPU and GPU. Also, dont forget PCI-e. It is the bottleneck for Parallel Programming implementation.
Hope this clarifies your doubts. Any more questions, feel free to ask.
I want to run a number of benchmarks on a multi-core system running Linux. I want to reserve one of the cores for my benchmarks. I know that I can use sched_setaffinity to limit my benchmarks to that core. How can I keep all other processes off my core? In other words, how can I set the default affinity of all processes to not include my core?
Even if you keep all the other processes off your "reserved for benchmarking" core, bear in mind that you can't stop them from consuming a variable and unpredictable proportion of the limited memory bandwidth to a multi-core chip, and that you can't stop them making variable demands on the shared L2 and L3 caches.
IMHO reproducible, scientific benchmarking needs a machine all to itself.
I have my own multithreaded C program which scales in speed smoothly with the number of CPU cores.. I can run it with 1, 2, 3, etc threads and get linear speedup.. up to about 5.5x speed on a 6-core CPU on a Ubuntu Linux box.
I had an opportunity to run the program on a very high end Sunfire x4450 with 4 quad-core Xeon processors, running Red Hat Enterprise Linux. I was eagerly anticipating seeing how fast the 16 cores could run my program with 16 threads..
But it runs at the same speed as just TWO threads!
Much hair-pulling and debugging later, I see that my program really is creating all the threads, they really are running simultaneously, but the threads themselves are slower than they should be. 2 threads runs about 1.7x faster than 1, but 3, 4, 8, 10, 16 threads all run at just net 1.9x! I can see all the threads are running (not stalled or sleeping), they're just slow.
To check that the HARDWARE wasn't at fault, I ran SIXTEEN copies of my program independently, simultaneously. They all ran at full speed. There really are 16 cores and they really do run at full speed and there really is enough RAM (in fact this machine has 64GB, and I only use 1GB per process).
So, my question is if there's some OPERATING SYSTEM explanation, perhaps some per-process resource limit which automatically scales back thread scheduling to keep one process from hogging the machine.
Clues are:
My program does not access the disk or network. It's CPU limited. Its speed scales linearly on a
single CPU box in Ubuntu Linux with
a hexacore i7 for 1-6 threads. 6
threads is effectively 6x speedup.
My program never runs faster than
2x speedup on this 16 core Sunfire
Xeon box, for any number of threads
from 2-16.
Running 16 copies of
my program single threaded runs
perfectly, all 16 running at once at
full speed.
top shows 1600% of
CPUs allocated. /proc/cpuinfo shows
all 16 cores running at full 2.9GHz
speed (not low frequency idle speed
of 1.6GHz)
There's 48GB of RAM free, it is not swapping.
What's happening? Is there some process CPU limit policy? How could I measure it if so?
What else could explain this behavior?
Thanks for your ideas to solve this, the Great Xeon Slowdown Mystery of 2010!
My initial guess would be shared memory bottlenecks. From what you say, your performance pretty much flatlines after 2 CPUs. You initially blame Redhat, but I'd be curious to see what happens if you install Ubuntu on the same hardware. I assume, of course, that you're running 64 bit SMP kernels across both tests.
It's probably not possible that the motherboard would peak at utilizing 2 CPUs. You have another machine with multiple cores that has provided better performance. Do you have hyperthreading turned on with the new machine? (and how does that answer compare to the old machine?). You're not, by chance, running in a virtualized environment?
Overall, your evidence is pointing to a ludicrously slow bottleneck somewhere. As you said, you're not I/O bound, so that leaves the CPU and memory. Either something is wrong with the hardware, or something is wrong with the hardware. Test one by changing the other, and you'll narrow down your possibilities quickly.
Do some research on rlimit - it's quite possible the shell/user acct you're running in has some RH-default or admin-set resource limits in place.
When you see this kind of odd scaling behaviour, especially if problems are seen with multiple threads, but not multiple processes, one thing to start looking at is the impacts of lock contention and other synchronisation primitives, which can cause threads running on different processors to have to wait for each other, potentially forcing multiple cores to flush their cache to main memory.
This means memory architecture starts to come into play, and that's going to be substantially faster when you have 6 cores on a single piece of silicon than when you're coordinating across 4 separate processors. Specifically, the single CPU case likely isn't needing to hit main memory for locking operations at all - everything is likely being handled at the L3 cache level, allowing the CPU to get on with things while data is flushed to main memory in the background.
While I expect the OP has lost interest in the question after all this time (or may not even have access to the hardware any more), one way to check this would be to see if the scaling up to 4 threads improves if the process affinity is set to lock it to a single physical CPU. Even better though would be to profile the application itself to see where it is spending it's time.As you change architectures and increase the number of cores, it gets harder and harder to guess where the bottlenecks are, so you really need to start measuring things directly, as in this example: http://postgresql.1045698.n5.nabble.com/Sun-Donated-a-Sun-Fire-T2000-to-the-PostgreSQL-community-td2057445.html