Develop Reference

Why lock is 240% faster than ReaderWriterLockSlim?

I have read another SO question: When is ReaderWriterLockSlim better than a simple lock?
And it does not explain exactly why ReaderWriterLockSlim so slow compared to lock.
My test is yes - testing with zero contention but still it doesnt explain the staggering difference.
Read lock takes 2.7s, Write lock 2.2s, lock 1.0s
This is complete code:
using System;
using System.Diagnostics;
using System.Threading;
namespace test
{
internal class Program
{
static int[] data = new int[100000000];
static object lock1 = new object();
static ReaderWriterLockSlim lock2 = new ReaderWriterLockSlim();
static void Main(string[] args)
{
for (int z = 0; z < 3; z++)
{
var sw = Stopwatch.StartNew();
for (int i = 0; i < data.Length; i++)
{
lock (lock1)
{
data[i] = i;
}
}
sw.Stop();
Console.WriteLine("Lock: {0}", sw.Elapsed);
sw.Restart();
for (int i = 0; i < data.Length; i++)
{
try
{
lock2.EnterReadLock();
data[i] = i;
}
finally
{
lock2.ExitReadLock();
}
}
sw.Stop();
Console.WriteLine("Read: {0}", sw.Elapsed);
sw.Restart();
for (int i = 0; i < data.Length; i++)
{
try
{
lock2.EnterWriteLock();
data[i] = i;
}
finally
{
lock2.ExitWriteLock();
}
}
sw.Stop();
Console.WriteLine("Write: {0}\n", sw.Elapsed);
}
Console.ReadKey(false);
}
}
}

You are looking at two devices. At the left is a lock. At the right is a ReaderWriterLockSlim.
The device at the left is used to control a single electric lamp from a single location. The device at the right is used to control two lamps from two different locations.¹ The device at the left is cheaper to buy, it requires less wiring, it is simpler to install and operate, and it loses less energy due to heat than the device at the right.
The analogy with the SPST/DPDT electric switches is probably far from perfect, but my point is that a lock is comparatively a simpler mechanism than the ReaderWriterLockSlim. It is used to enforce a single policy to a homogenous group of worker threads. On the other hand a ReaderWriterLockSlim is used to enforce two different policies to two separate groups of workers (readers and writers), regarding to how they interact with members of the same group and the other group. It should be of no big surprise that the more complex mechanism has a higher operational cost (overhead) than the simpler mechanism. That's the cost that you have to pay in order to get finer control of the worker threads.
¹ Or maybe not. I am not an electrician!

Thanks to canton7 and Kevin Gosse, I found my 2013 question perfectly answered by Hans Passant: When exactly does .NET Monitor go to kernel-mode?
So lock is faster in a no-contention scenario simply because it has lighter logic and kernel mode is not involved.

Measuring execution time of other processes with C#, odd results

I'm trying to build a small benchmarking application, which allows the user to measure the execution time and memory usage of a program.
This is the code to measure the execution time:
private static Stopwatch _stopwatch;
static void Main(string[] args]
{
_stopwatch = new Stopwatch();
//only for this test
Console.WriteLine(TimeProcess("Empty.exe"));
Console.WriteLine(TimeProcess("Sieve.exe"));
Console.ReadKey();
}
private static long TimeProcess(String name)
{
Process process = new Process();
process.StartInfo.FileName = name;
_stopwatch.Reset();
_stopwatch.Start();
process.Start();
process.WaitForExit();
_stopwatch.Stop();
return _stopwatch.ElapsedMilliseconds;
}
To see if the code is working properly, I decided to implement the "Sieve of Eratosthenes" algorithm. I implemented it twice, one time with a built in stopwatch, and one time without.
Sieve
int[] numbersToTest = Enumerable.Range(0, 1000).ToArray();
int posInArray = 2;
while (numbersToTest[posInArray] != numbersToTest[numbersToTest.Length - 1])
{
numbersToTest = numbersToTest.Where(x => x % numbersToTest[posInArray] != 0 || x == numbersToTest[posInArray]).ToArray();
posInArray++;
}
TimedSieve:
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
int[] numbersToTest = Enumerable.Range(0, 1000).ToArray();
int posInArray = 2;
while (numbersToTest[posInArray] != numbersToTest[numbersToTest.Length - 1])
{
numbersToTest = numbersToTest.Where(x => x % numbersToTest[posInArray] != 0 || x == numbersToTest[posInArray]).ToArray();
posInArray++;
}
stopwatch.Stop();
Console.WriteLine(stopwatch.ElapsedTicks);
Moreover, I have a project with an empty main method. My logic was, that when I measure the execution of the "Sieve" and subtract the time of the empty project, the resulting number should be roughly the same as the number measured by the "TimedSieve".
So I started measuring...
Empty: 79 milliseconds
Sieve: 53 milliseconds
TimedSieve: 4 milliseconds
Obviously these results seem very fishy:
The TimedSieve is a lot faster than both the empty project and the Sieve
The empty project is slower than the Sieve!
Just out of curiosity I also timed the Sieve and the empty project using Powershells "Measure-Command"
Sieve: 25 milliseconds
Empty: 17 milliseconds
Something I noticed was that the fact that the order in which the processes were measured influenced the results, the process which was measured first always lost. I also noticed that moving the start of the stopwatch after the start of the process like this
process.Start();
_stopwatch.Start();
got rid of the aforementioned effect (Empty is now always faster than Sieve) and produced numbers which are a lot closer to the results of the other measurement methods
Empty: 34
Sieve: 42
While trying to solve the problem, I also read that benchmarks should include a "warmup" round, and I decided to benchmark both programs multiple times and take the average to get better results.
static void Main(string[] args)
{
_stopwatch = new Stopwatch();
//discard results of the first run
TimeProcess("Sieve.exe");
long sum = 0;
for (int i = 0; i < 100; i++)
{
sum += TimeProcess("Sieve.exe");
}
Console.WriteLine(sum/100);
TimeProcess("Empty.exe");
sum = 0;
for (int i = 0; i < 100; i++)
{
sum += TimeProcess("Empty.exe");
}
Console.WriteLine(sum/100);
Console.ReadKey();
}
This got rid of the "empty slower than sieve" effect, which is why I decided to start the stopwatch before the process again.
How can I improve this code to get reliable results? While the numbers have gotten a lot more reasonable, they are still slower than both the Powershell and the TimedSieve measurements.

Measuring the execution time of some another process in a non-real system OS can lead to different inconsistent results. That is just the nature of systems without(mostly) timing guarantees.
You have already more or less dealt with warm-up(IO and IO caching related...) issues, and made results more statistically correct with multiple runs.
But TimeOf(Algo)doesn't truly equal toTimeOf(FullAppWithAlgo) - TimeOf(Empty).
It is close, but TimeOf(Algo) doesn't include:
Time spent to JIT the application with code and not just empty Main,
Time spent to initialize static classes(that doesn't occur if class is never used, like in Empty)
Time for other minor and major things that happen.
They may be small time spans, but they still add to the execution time of full app in a way that differs from Empty one.
Also, to make results more close to those given by PowerShell you may try to use Process.StartTime and Process.ExitTime in place of StopWatch while measuring full application runtime:
private static long TimeProcess(String name)
{
Process process = new Process();
process.StartInfo.FileName = name;
process.Start();
process.WaitForExit();
return (process.EndTime - process.StartTime).TotalMilliseconds;
}
It won't change the difference between the empty and the full app, but will give more consistent timings for each run, because you won't have to wait for notification by OS, that obviously occurs some time after the app has already ended.

Java is scaling much worse than C# over many cores?

I am testing spawning off many threads running the same function on a 32 core server for Java and C#. I run the application with 1000 iterations of the function, which is batched across either 1,2,4,8, 16 or 32 threads using a threadpool.
At 1, 2, 4, 8 and 16 concurrent threads Java is at least twice as fast as C#. However, as the number of threads increases, the gap closes and by 32 threads C# has nearly the same average run-time, but Java occasionally takes 2000ms (whereas both languages are usually running about 400ms). Java is starting to get worse with massive spikes in the time taken per thread iteration.
EDIT This is Windows Server 2008
EDIT2 I have changed the code below to show using the Executor Service threadpool. I have also installed Java 7.
I have set the following optimisations in the hotspot VM:
-XX:+UseConcMarkSweepGC -Xmx 6000
but it still hasnt made things any better. The only difference between the code is that im using the below threadpool and for the C# version we use:
http://www.codeproject.com/Articles/7933/Smart-Thread-Pool
Is there a way to make the Java more optimised? Perhaos you could explain why I am seeing this massive degradation in performance?
Is there a more efficient Java threadpool?
(Please note, I do not mean by changing the test function)
import java.io.DataOutputStream;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.PrintStream;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.ThreadPoolExecutor;
public class PoolDemo {
static long FastestMemory = 2000000;
static long SlowestMemory = 0;
static long TotalTime;
static int[] FileArray;
static DataOutputStream outs;
static FileOutputStream fout;
static Byte myByte = 0;
public static void main(String[] args) throws InterruptedException, FileNotFoundException {
int Iterations = Integer.parseInt(args[0]);
int ThreadSize = Integer.parseInt(args[1]);
FileArray = new int[Iterations];
fout = new FileOutputStream("server_testing.csv");
// fixed pool, unlimited queue
ExecutorService service = Executors.newFixedThreadPool(ThreadSize);
ThreadPoolExecutor executor = (ThreadPoolExecutor) service;
for(int i = 0; i<Iterations; i++) {
Task t = new Task(i);
executor.execute(t);
}
for(int j=0; j<FileArray.length; j++){
new PrintStream(fout).println(FileArray[j] + ",");
}
}
private static class Task implements Runnable {
private int ID;
public Task(int index) {
this.ID = index;
}
public void run() {
long Start = System.currentTimeMillis();
int Size1 = 100000;
int Size2 = 2 * Size1;
int Size3 = Size1;
byte[] list1 = new byte[Size1];
byte[] list2 = new byte[Size2];
byte[] list3 = new byte[Size3];
for(int i=0; i<Size1; i++){
list1[i] = myByte;
}
for (int i = 0; i < Size2; i=i+2)
{
list2[i] = myByte;
}
for (int i = 0; i < Size3; i++)
{
byte temp = list1[i];
byte temp2 = list2[i];
list3[i] = temp;
list2[i] = temp;
list1[i] = temp2;
}
long Finish = System.currentTimeMillis();
long Duration = Finish - Start;
TotalTime += Duration;
FileArray[this.ID] = (int)Duration;
System.out.println("Individual Time " + this.ID + " \t: " + (Duration) + " ms");
if(Duration < FastestMemory){
FastestMemory = Duration;
}
if (Duration > SlowestMemory)
{
SlowestMemory = Duration;
}
}
}
}

Summary
Below are the original response, update 1, and update 2. Update 1 talks about dealing with the race conditions around the test statistic variables by using concurrency structures. Update 2 is a much simpler way of dealing with the race condition issue. Hopefully no more updates from me - sorry for the length of the response but multithreaded programming is complicated!
Original Response
The only difference between the code is that im using the below
threadpool
I would say that is an absolutely huge difference. It's difficult to compare the performance of the two languages when their thread pool implementations are completely different blocks of code, written in user space. The thread pool implementation could have enormous impact on performance.
You should consider using Java's own built-in thread pools. See ThreadPoolExecutor and the entire java.util.concurrent package of which it is part. The Executors class has convenient static factory methods for pools and is a good higher level interface. All you need is JDK 1.5+, though the newer, the better. The fork/join solutions mentioned by other posters are also part of this package - as mentioned, they require 1.7+.
Update 1 - Addressing race conditions by using concurrency structures
You have race conditions around the setting of FastestMemory, SlowestMemory, and TotalTime. For the first two, you are doing the < and > testing and then the setting in more than one step. This is not atomic; there is certainly the chance that another thread will update these values in between the testing and the setting. The += setting of TotalTime is also non-atomic: a test and set in disguise.
Here are some suggested fixes.
TotalTime
The goal here is a threadsafe, atomic += of TotalTime.
// At the top of everything
import java.util.concurrent.atomic.AtomicLong;
...
// In PoolDemo
static AtomicLong TotalTime = new AtomicLong();
...
// In Task, where you currently do the TotalTime += piece
TotalTime.addAndGet (Duration);
FastestMemory / SlowestMemory
The goal here is testing and updating FastestMemory and SlowestMemory each in an atomic step, so no thread can slip in between the test and update steps to cause a race condition.
Simplest approach:
Protect the testing and setting of the variables using the class itself as a monitor. We need a monitor that contains the variables in order to guarantee synchronized visibility (thanks #A.H. for catching this.) We have to use the class itself because everything is static.
// In Task
synchronized (PoolDemo.class) {
if (Duration < FastestMemory) {
FastestMemory = Duration;
}
if (Duration > SlowestMemory) {
SlowestMemory = Duration;
}
}
Intermediate approach:
You may not like taking the whole class for the monitor, or exposing the monitor by using the class, etc. You could do a separate monitor that does not itself contain FastestMemory and SlowestMemory, but you will then run into synchronization visibility issues. You get around this by using the volatile keyword.
// In PoolDemo
static Integer _monitor = new Integer(1);
static volatile long FastestMemory = 2000000;
static volatile long SlowestMemory = 0;
...
// In Task
synchronized (PoolDemo._monitor) {
if (Duration < FastestMemory) {
FastestMemory = Duration;
}
if (Duration > SlowestMemory) {
SlowestMemory = Duration;
}
}
Advanced approach:
Here we use the java.util.concurrent.atomic classes instead of monitors. Under heavy contention, this should perform better than the synchronized approach. Try it and see.
// At the top of everything
import java.util.concurrent.atomic.AtomicLong;
. . . .
// In PoolDemo
static AtomicLong FastestMemory = new AtomicLong(2000000);
static AtomicLong SlowestMemory = new AtomicLong(0);
. . . . .
// In Task
long temp = FastestMemory.get();
while (Duration < temp) {
if (!FastestMemory.compareAndSet (temp, Duration)) {
temp = FastestMemory.get();
}
}
temp = SlowestMemory.get();
while (Duration > temp) {
if (!SlowestMemory.compareAndSet (temp, Duration)) {
temp = SlowestMemory.get();
}
}
Let me know what happens after this. It may not fix your problem, but the race condition around the very variables that track your performance is too dangerous to ignore.
I originally posted this update as a comment but moved it here so that I would have room to show code. This update has been through a few iterations - thanks to A.H. for catching a bug I had in an earlier version. Anything in this update supersedes anything in the comment.
Last but not least, an excellent source covering all this material is Java Concurrency in Practice, the best book on Java concurrency, and one of the best Java books overall.
Update 2 - Addressing race conditions in a much simpler way
I recently noticed that your current code will never terminate unless you add executorService.shutdown(). That is, the non-daemon threads living in that pool must be terminated or else the main thread will never exit. This got me to thinking that since we have to wait for all threads to exit, why not compare their durations after they finished, and thus bypass the concurrent updating of FastestMemory, etc. altogether? This is simpler and could be faster; there's no more locking or CAS overhead, and you are already doing an iteration of FileArray at the end of things anyway.
The other thing we can take advantage of is that your concurrent updating of FileArray is perfectly safe, since each thread is writing to a separate cell, and since there is no reading of FileArray during the writing of it.
With that, you make the following changes:
// In PoolDemo
// This part is the same, just so you know where we are
for(int i = 0; i<Iterations; i++) {
Task t = new Task(i);
executor.execute(t);
}
// CHANGES BEGIN HERE
// Will block till all tasks finish. Required regardless.
executor.shutdown();
executor.awaitTermination(10, TimeUnit.SECONDS);
for(int j=0; j<FileArray.length; j++){
long duration = FileArray[j];
TotalTime += duration;
if (duration < FastestMemory) {
FastestMemory = duration;
}
if (duration > SlowestMemory) {
SlowestMemory = duration;
}
new PrintStream(fout).println(FileArray[j] + ",");
}
. . .
// In Task
// Ending of Task.run() now looks like this
long Finish = System.currentTimeMillis();
long Duration = Finish - Start;
FileArray[this.ID] = (int)Duration;
System.out.println("Individual Time " + this.ID + " \t: " + (Duration) + " ms");
Give this approach a shot as well.
You should definitely be checking your C# code for similar race conditions.

...but Java occasionally takes 2000ms...
And
byte[] list1 = new byte[Size1];
byte[] list2 = new byte[Size2];
byte[] list3 = new byte[Size3];
The hickups will be the garbage collector cleaning up your arrays. If you really want to tune that I suggest you use some kind of cache for the arrays.
Edit
This one
System.out.println("Individual Time " + this.ID + " \t: " + (Duration) + " ms");
does one or more synchronized internally. So your highly "concurrent" code will be serialized quite good at this point. Just remove it and retest.

While #sparc_spread's answer is great, another thing I've noticed is this:
I run the application with 1000 iterations of the function
Notice that the HotSpot JVM is working on interpreted mode for the first 1.5k iterations of any function on client mode, and for 10k iterations on server mode. Computers with that many cores are automatically considered "servers" by the HotSpot JVM.
That would mean that C# would do JIT (and run in machine code) before Java does, and has a chance for better performance at the function runtime. Try increasing the iterations to 20,000 and start counting from 10k iteration.
The rationale here is that the JVM collects statistical data for how to do JIT best. It trusts that your function is going to be run a lot through time, so it takes a "slow bootstrapping" mechanism for a faster runtime overall. Or in their words "20% of the functions run 80% of the time", so why JIT them all?

Are you using java6? Java 7 comes with features to improve performance in parallel programing:
http://www.oracle.com/technetwork/articles/java/fork-join-422606.html

Why is DynamicMethod so much slower on x64?

I have a WCF service that uses LINQ to SQL for its data layer. Only stored procedures are in use, no dynamic table access. When I target x64, I am getting half the throughput of an x86 build. I have traced the hot path to Reflection.Emit.DynamicMethod.CreateDelegate. I created a simple test project to demonstrate the difference in performance between the two platforms.
What is the specific explanation for DynamicMethod being so much slower on x64? My vague understanding is that there may be an additional thunk involved in DynamicInvoke on x64.
Here are the results when performed on Windows 7 Enterprise x64, Core i7 Q720 # 1.60 GHz, single-threaded:
Build Target Average milliseconds to execute 100,000 iterations
x86 5504
x64 14699
Any CPU 14789
And the test code:
class Program
{
private delegate string XInvoker(string arg);
private const int OUTER_ITERATIONS = 4;
private const int INNER_ITERATIONS = 100000;
static void Main(string[] args)
{
Console.WriteLine("Timing {0} iterations, repeat {1} times...", INNER_ITERATIONS, OUTER_ITERATIONS);
var watch = new Stopwatch();
long totalMs = 0;
for (int outer = 0; outer < OUTER_ITERATIONS; outer++)
{
watch.Restart();
for (int inner = 0; inner < INNER_ITERATIONS; inner++)
{
var method = new DynamicMethod("X", typeof(string), new[] { typeof(string) });
var ilGen = method.GetILGenerator();
ilGen.Emit(OpCodes.Ldarg_0);
ilGen.Emit(OpCodes.Ret);
var del = method.CreateDelegate(typeof(XInvoker));
var blah = del.DynamicInvoke("blah");
}
watch.Stop();
totalMs += watch.ElapsedMilliseconds;
Console.WriteLine("Took {0} ms to iterate {1} times", watch.ElapsedMilliseconds, INNER_ITERATIONS);
}
Console.WriteLine();
Console.WriteLine("Overall average: {0} ms to iterate {1} times", totalMs / OUTER_ITERATIONS, INNER_ITERATIONS);
}
}

I would guess it's to do with the speed of compilation. There are lots of threads that seem to indicate JIT compiling for x64 is significanltly slower than x86.
In this one case someone saw significant performance increase in their x64 JIT just because other dependent assemblies weren't NGEN'd. Although I doubt it would help in this scenario, you never know what other things it is trying to load that might be slowing it down. Perhaps try running the command in the answer and see if that changes your performance.
WPF slow to start on x64 in .NET Framework 4.0

Why is concurrent modification of arrays so slow?

I was writing a program to illustrate the effects of cache contention in multithreaded programs. My first cut was to create an array of long and show how modifying adjacent items causes contention. Here's the program.
const long maxCount = 500000000;
const int numThreads = 4;
const int Multiplier = 1;
static void DoIt()
{
long[] c = new long[Multiplier * numThreads];
var threads = new Thread[numThreads];
// Create the threads
for (int i = 0; i < numThreads; ++i)
{
threads[i] = new Thread((s) =>
{
int x = (int)s;
while (c[x] > 0)
{
--c[x];
}
});
}
// start threads
var sw = Stopwatch.StartNew();
for (int i = 0; i < numThreads; ++i)
{
int z = Multiplier * i;
c[z] = maxCount;
threads[i].Start(z);
}
// Wait for 500 ms and then access the counters.
// This just proves that the threads are actually updating the counters.
Thread.Sleep(500);
for (int i = 0; i < numThreads; ++i)
{
Console.WriteLine(c[Multiplier * i]);
}
// Wait for threads to stop
for (int i = 0; i < numThreads; ++i)
{
threads[i].Join();
}
sw.Stop();
Console.WriteLine();
Console.WriteLine("Elapsed time = {0:N0} ms", sw.ElapsedMilliseconds);
}
I'm running Visual Studio 2010, program compiled in Release mode, .NET 4.0 target, "Any CPU", and executed in the 64-bit runtime without the debugger attached (Ctrl+F5).
That program runs in about 1,700 ms on my system, with a single thread. With two threads, it takes over 25 seconds. Figuring that the difference was cache contention, I set Multipler = 8 and ran again. The result is 12 seconds, so contention was at least part of the problem.
Increasing Multiplier beyond 8 doesn't improve performance.
For comparison, a similar program that doesn't use an array takes only about 2,200 ms with two threads when the variables are adjacent. When I separate the variables, the two thread version runs in the same amount of time as the single-threaded version.
If the problem was array indexing overhead, you'd expect it to show up in the single-threaded version. It looks to me like there's some kind of mutual exclusion going on when modifying the array, but I don't know what it is.
Looking at the generated IL isn't very enlightening. Nor was viewing the disassembly. The disassembly does show a couple of calls to (I think) the runtime library, but I wasn't able to step into them.
I'm not proficient with windbg or other low-level debugging tools these days. It's been a really long time since I needed them. So I'm stumped.
My only hypothesis right now is that the runtime code is setting a "dirty" flag on every write. It seems like something like that would be required in order to support throwing an exception if the array is modified while it's being enumerated. But I readily admit that I have no direct evidence to back up that hypothesis.
Can anybody tell me what is causing this big slowdown?

You've got false sharing. I wrote an article about it here