search for: 750000

I do not understand what happens here (under Win XP): a <- abs(matrix(rnorm(800*800)/2, ncol=800, nrow=800)) system.time(b <- a^1000)[3] took about 1 sec on my computer with R 1.7.0 and it takes now 4.59 sec with R 1.7.1 Similarly, phi <- 1.6180339887498949 a <- floor(runif(750000)*1000) system.time(b <- (phi^a - (-phi)^(-a))/sqrt(5))[3] took about 0.9 sec with R 1.7.0, and it takes 11.8 sec (!!!) in R 1.7.1. Are there some changes made between 1.7.0 and 1.7.1 that could cause such a large difference in time to do such simple computations??? Best, Philippe Grosjean...

I do not understand what happens here (under Win XP): a <- abs(matrix(rnorm(800*800)/2, ncol=800, nrow=800)) system.time(b <- a^1000)[3] took about 1 sec on my computer with R 1.7.0 and it takes now 4.59 sec with R 1.7.1 Similarly, phi <- 1.6180339887498949 a <- floor(runif(750000)*1000) system.time(b <- (phi^a - (-phi)^(-a))/sqrt(5))[3] took about 0.9 sec with R 1.7.0, and it takes 11.8 sec (!!!) in R 1.7.1. Are there some changes made between 1.7.0 and 1.7.1 that could cause such a large difference in time to do such simple computations??? Best, Philippe Grosjean...

Dear R people. I´m facing a big problem. I need to create a matrix with 10.000 columns and 750.000 rows. matrix<- as.data.frame(matrix(data=0L, nrow=750000, ncol=10000) as you can see, the data frame has huge dimesions. I was able to find out about thr "L" in data, this way I´m telling that my data is "integer" class. By doing this object.size(matrix) tells me that this is half of the matrix when I don´t define as integer (L) The d...

Hi all, Disregard my previous posts, I've consolidated everything here. I'm having terrible performance issues with samba over a WAN (point-to-point T1 link). Doing a copy of a 2MB file from a samba server to a linux client running smbclient takes over 5 minutes. SCPing the same file takes seconds. The server is running samba version 3.0.25c with kernel 2.6.16.18. I've put up a set

...y tcng configuration file. could someone verify the syntax for building a kernel module? i did it as shown below ... module gets built but i cannot load it. thanks charles ps -- sorry, don''t know c++ :-) [root]# cat /etc/tcng.test #define LAN "eth0" #define LAN_INGRESS 750000 #define LAN_EGRESS 750000 dev LAN { egress { class ( <$adsl_high> ) if 1; htb ( ) { class ( rate LAN_EGRESS kbps, ceil LAN_EGRESS kbps ) { $adsl_high = class ( prio 1, rate LAN_EGRESS kbps, burst 6kB, ceil LAN_EGRESS kbps ) { sfq ( pertu...

Hi,? I have two big data set.? data _1 :? > dim(data_1) [1] 15820 5 > head(data_1) ? ?Chromosome ?????Start????????End????????Feature GroupA_3 1: ? ? ? ????????chr1 521369 ?750000 ????chr1-0001 ? ?????0.170 2: ? ? ? ????????chr1 750001 ?800000 ????chr1-0002 ? ????-0.086 3: ? ? ? ????????chr1 800001 ?850000 ????chr1-0003 ? ?????0.006 4: ? ? ? ????????chr1 850001 ?900000 ????chr1-0004 ? ?????0.050 5: ? ? ? ????????chr1 900001 ?950000 ????chr1-0005 ? ?????0.062 6: ? ? ? ???????...

...lks, I'm checking the structure of a dataframe for duplicate parameters at a site station (i.e depth should be measured once, not twice), using aggregate to count each parameter within a site station. The fake data below has only 26000 rows, and takes roughly 14 seconds. My real data has 750000 rows and I had to stop execution after about an hour. The by() function is faster, but I do not understand how to accurately associate those results with my test data. How can I get this to work faster? I can't shake the feeling that it's something simple. Thanks for any pointers. df...

I have both the CRAN binary and my own compiled version of R v1.7.1 on the same machine (Dell Pentium III 800 MHz running NT 4) Using the example provided earlier: > phi <- 1.6180339887498949 > a <- floor(runif(750000)*1000) > system.time(b <- (phi^a - (-phi)^(-a))/sqrt(5))[3] I get 10.99 secs on the CRAN binary and 2.09 secs on my own compiled version. I hope this helps someone ... Regards, John Marsland > -----Original Message----- > From: Prof Brian D Ripley [mailto:ripley@stats.ox.ac.uk] &g...

Adds reclocking for NVA0, and a whole lot of work for other cards. Had these patches collecting dust for a little, but tested them with both my NVA0, and Martin's a while back. Success not guaranteed, but should be quite a leap forward. Happy reviewing and testing. Cheers, Roy

...abf00 002abf55 002abfaa 0x0032e93c: 002abfff 002adf00 002adf55 002adfaa Backtrace: =>0 0x00008080 (0x00800080) 0x00008080: -- no code accessible -- Modules: Module Address Debug info Name (25 modules) PE 3c0000- 3f6000 Deferred rfwin32 PE 400000- 411000 Deferred fuw32 PE 750000- 78f000 Deferred rftr32 PE 8a0000- 915000 Deferred rfifax32 PE 10000000-100c5000 Deferred rfaxui PE 7b810000-7b91a000 Deferred kernel32 PE 7bc10000-7bc14000 Deferred ntdll PE 7c340000-7c396000 Deferred msvcr71 PE 7ddd0000-7ddd3000 Deferred spools...

Signed-off-by: Roy Spliet <rspliet at eclipso.eu> --- drivers/gpu/drm/nouveau/include/nvkm/subdev/bios/ramcfg.h | 1 + drivers/gpu/drm/nouveau/nvkm/subdev/bios/rammap.c | 3 ++- drivers/gpu/drm/nouveau/nvkm/subdev/bios/timing.c | 2 ++ drivers/gpu/drm/nouveau/nvkm/subdev/fb/gddr3.c | 6 ++++-- 4 files changed, 9 insertions(+), 3 deletions(-) diff --git

...0000000 00000518 00000000 Backtrace: =>1 0x6ff5824c in fog (+0x824c) (0x0443c000) 2 0x00000000 (0x00000000) 0x6ff5824c: cmpl %edi,0xbf8(%ebp,%ebx,4) Modules: Module Address Debug info Name (104 modules) PE 400000- 634000 Deferred d2gs PE 750000- 857000 Deferred d2cmp PE 12f0000- 1417000 Deferred d2game PE 1fb0000- 1fd6000 Deferred plugy PE 60000000-6002e000 Deferred ijl11 PE 68000000-68017000 Deferred d2server PE 6f8a0000-6f970000 Deferred...

...gt; > I have both the CRAN binary and my own compiled version of > R v1.7.1 on the > > same machine (Dell Pentium III 800 MHz running NT 4) > > > > Using the example provided earlier: > > > > > phi <- 1.6180339887498949 > > > a <- floor(runif(750000)*1000) > > > system.time(b <- (phi^a - (-phi)^(-a))/sqrt(5))[3] > > > > I get 10.99 secs on the CRAN binary and 2.09 secs on my own compiled > > version. > > > > I hope this helps someone ... > > > > Regards, > > > > John Marsla...

...410000 220000 400000 380000 500000 1000000 520000 690000 500000 520000 260000 630000 630000 410000 290000 1300000 860000 600000 450000 330000 390000 580000 270000 460000 360000 300000 3600000 540000 370000 460000 150000 560000 630000 810000 1300000 470000 620000 520000 540000 440000 1900000 760000 750000 440000 550000 3300000 780000 3600000 1400000 840000 450000 970000 610000 490000 280000 960000 350000 1100000 770000 510000 460000 590000 760000 610000 550000 460000 470000 650000 370000 170000 630000 530000 720000 850000 570000 860000 6000000 1700000 1400000 470000 1200000 360000 370000 1100000 310...

...410000 220000 400000 380000 500000 1000000 520000 690000 500000 520000 260000 630000 630000 410000 290000 1300000 860000 600000 450000 330000 390000 580000 270000 460000 360000 300000 3600000 540000 370000 460000 150000 560000 630000 810000 1300000 470000 620000 520000 540000 440000 1900000 760000 750000 440000 550000 3300000 780000 3600000 1400000 840000 450000 970000 610000 490000 280000 960000 350000 1100000 770000 510000 460000 590000 760000 610000 550000 460000 470000 650000 370000 170000 630000 530000 720000 850000 570000 860000 6000000 1700000 1400000 470000 1200000 360000 370000 1100000 310...

...survey library(survey) # creating data listc <- c("Denmark","Finland","Norway","Sweden","Denmark","Finland","Norway","Sweden") listw <- c(1,2,1,1,1,1,1,1) listd <- c(0,0,0,0,1000,1000,1000,2000) listt <- c(750000,500000,900000,1900000,5000,5000,5000,10000) list.cwdt <- c(listc, listw, listd, listt) country <- data.frame(country=listc,weight=listw,deaths=listd,yrs_at_risk=listt) # running a frequency weighted regression to get the correct point estimates for comparison glm <- glm(deaths ~ country +...

...b7db857e __clone+0x5e() in libc.so.6 (0x00000000) 0x017c45d0: addb %al,0x0(%eax) Modules: Module Address Debug info Name (96 modules) PE 3b0000- 3e1000 Deferred igdi PE 400000- 41e000 Deferred scm_2_0_24_setup_englisch PE 750000- 777000 Deferred _setup PE 780000- 84e000 Deferred ikernel PE 960000- 971000 Deferred ctor PE a90000- ad4000 Deferred iscript PE e10000- e77000 Deferred isrt PE 10a0000- 10ce000 Deferred...

...7e0) 3 0x10d9ebb4 in bioshock (+0x49ebb3) (0x0073f870) 4 0x7edd738c call_process_entry+0xb() in kernel32 (0x0073f8c8) 5 0x11457220 in bioshock (+0xb5721f) (0x0073fd44) 0x1093f222: movl 0x10(%esi),%ecx Modules: Module Address Debug info Name (101 modules) PE 750000- abf000 Deferred d3dx9_33 PE ac0000- ad6000 Deferred xinput1_3 PE 10000000-1003e000 Deferred paul PE 10900000-11efb000 Export bioshock PE 18000000-18038000 Deferred binkw32 PE 71590000-71617000 Def...

...comm PE 380000-3b1000 Deferred eslib PE 3c0000-3ea000 Deferred esdlg PE 3f0000-400000 Deferred esimgtophn PE 400000-408000 Deferred pcstudio PE 410000-642000 Deferred esmain PE 650000-666000 Deferred esmeltophn PE 670000-748000 Deferred esmediaobj PE 750000-a4a000 Deferred m5_emusmw5 PE a50000-b4a000 Deferred esfm PE b50000-b98000 Deferred esimportexport PE ba0000-bb2000 Deferred esoutlookimpexp PE bc0000-bd5000 Deferred esliveupdate PE 1050000-105b000 Deferred esr0804 PE 10000000-1002d000 Export esda...

On Thu, Jun 09, 2011 at 08:47:26PM -0400, Jack Howarth wrote: > Duncan, > Here are the complete benchmarks rerun against gcc 4.5.4 built with... > > Using built-in specs. > COLLECT_GCC=gfortran-fsf-4.5 > COLLECT_LTO_WRAPPER=/sw/lib/gcc4.5/libexec/gcc/x86_64-apple-darwin11.0.0/4.5.4/lto-wrapper > Target: x86_64-apple-darwin11.0.0 > Configured with: