benchmarking

So I wanted to benchmark some basic java functionality to add some imformation to this question: What is the gain from declaring a method as static . I know writing benchmarks is sometimes not easy but what happens here I cannot explain. Please note that I'm not interessted in how to fix this but on why this happens* The Test class: public class TestPerformanceOfStaticVsDynamicCalls { private static final long RUNS = 1_000_000_000L; public static void main( String [] args ){ new TestPerformanceOfStaticVsDynamicCalls().run(); } private void run(){ long r=0; long start, end; for( int loop = 0;

I have read a solution to this using tic(), toc() functions tic <- function(gcFirst = TRUE, type=c("elapsed", "user.self", "sys.self")) { type <- match.arg(type) assign(".type", type, envir=baseenv()) if(gcFirst) gc(FALSE) tic <- proc.time()[type] assign(".tic", tic, envir=baseenv()) invisible(tic) } toc <- function() { type <- get(".type", envir=baseenv()) toc <- proc.time()[type] tic <- get(".tic", envir=baseenv()) print(toc - tic) invisible(toc) } tic(); -----code---- toc(); elapsed 0.15 But I would like to get a lot of precision in milliseconds? Also I was using this ptm <- proc.time() --

I'm writing a Java applet to run differently under different hardware. For instance if I know a computer has a large amount of RAM but a weak processor, I can alter the balance of some time-memory trade-offs. Being able to discover the exact make and model of the CPU on which the applet is running could be helpful. Having such information would allow me to benchmark my software against different systems and find bottlenecks. Generally what I'm looking for is: Number of cores and/or processors 32 bit vs 64 bit CPU CPU Cache line size Size of L1, L2, L3 cache Set associativity of cache Size of

I'm performing matrix multiplication with this simple algorithm. To be more flexible I used objects for the matricies which contain dynamicly created arrays. Comparing this solution to my first one with static arrays it is 4 times slower. What can I do to speed up the data access? I don't want to change the algorithm. matrix mult_std(matrix a, matrix b) { matrix c(a.dim(), false, false); for (int i = 0; i < a.dim(); i++) for (int j = 0; j < a.dim(); j++) { int sum = 0; for (int k = 0; k < a.dim(); k++) sum += a(i,k) * b(k,j); c(i,j) = sum; } return c; } EDIT I corrected my Question avove! I

Consider: #include <time.h> #include <unistd.h> #include <iostream> using namespace std; const int times = 1000; const int N = 100000; void run() { for (int j = 0; j < N; j++) { } } int main() { clock_t main_start = clock(); for (int i = 0; i < times; i++) { clock_t start = clock(); run(); cout << "cost: " << (clock() - start) / 1000.0 << " ms." << endl; //usleep(1000); } cout << "total cost: " << (clock() - main_start) / 1000.0 << " ms." << endl; } Here is the example code. In the first 26 iterations of the timing loop, the run function costs about 0.4 ms, but then the cost reduces to 0.2 ms.