similar to: man: zfs receive -v

NFS server (b39): bash-3.00# zfs get quota nfs-s5-s8/d5201 nfs-s5-p0/d5110 NAME PROPERTY VALUE SOURCE nfs-s5-p0/d5110 quota 600G local nfs-s5-s8/d5201 quota 600G local bash-3.00# bash-3.00# df -h | egrep "d5201|d5110" nfs-s5-p0/d5110 600G 527G 73G 88% /nfs-s5-p0/d5110

Hello ZFS, System was rebooted and after reboot server again System is snv_39, SPARC, T2000 bash-3.00# ptree 7 /lib/svc/bin/svc.startd -s 163 /sbin/sh /lib/svc/method/fs-local 254 /usr/sbin/zfs mount -a [...] bash-3.00# zfs list|wc -l 46 Using df I can see most file systems are already mounted. > ::ps!grep zfs R 254 163 7 7 0 0x4a004000

Hi all. A quick question about the checksum error detection routines in ZFS. Surely ZFS can decide about checksum errors in a redundant environment but what about an non-redundant one? We connected a single RAID5 array to a v440 as a NFS server and while doing backups and the like we see the "zpool status -v" checksum error counters increment once in a while. Nevertheless the

Hi Jean-Marc, Thanks a lot for reviewing this huge assembly function! silk_warped_autocorrelation_FIX_c()'s kernel part is for( n = 0; n < length; n++ ) { tmp1_QS = silk_LSHIFT32( (opus_int32)input[ n ], QS ); /* Loop over allpass sections */ for( i = 0; i < order; i++ ) { /* Output of allpass section */ tmp2_QS = silk_SMLAWB(

This is a great idea. But the order (psEncC->shapingLPCOrder) can be configured to 12, 14, 16, 20 and 24 according to complexity parameter. It's hard to get a universal function to handle all these orders efficiently. Any suggestions? Thanks, Linfeng On Mon, Feb 6, 2017 at 12:40 PM, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi Linfeng, > > On 06/02/17 02:51 PM,

Hi Jean-Marc, Thanks for your suggestions. Will get back to you once we have some updates. Linfeng On Mon, Feb 6, 2017 at 5:47 PM, Jean-Marc Valin <jmvalin at jmvalin.ca> wrote: > Hi Linfeng, > > On 06/02/17 07:18 PM, Linfeng Zhang wrote: > > This is a great idea. But the order (psEncC->shapingLPCOrder) can be > > configured to 12, 14, 16, 20 and 24 according to

I attached a new patch with small cleanup (disassembly is identical as the last patch). We have done the same internal testing as usual. Also, attached 2 failed temporary versions which try to reduce code size (just for code review reference purpose). The new patch of silk_warped_autocorrelation_FIX_neon() has a code size of 3,228 bytes (with gcc). smaller_slower.c has a code size of 2,304

The following dummy data frame has factor Q (with 2 levels) nesting factor P (with levels p1 and p2 nested under q1, and p3 and p4 nested under q2), but both crossing the random variate s, which has 8 levels. The dependent measure is dv. > # The data frame: > testnest dv s P Q 1 1 s1 p1 q1 2 2 s2 p1 q1 3 1 s3 p1 q1 4 2 s4 p1 q1 5 1 s5 p1 q1 6 3 s6 p1 q1 7 3 s7

Hi, Attached is a patch with arm neon optimizations for silk_warped_autocorrelation_FIX(). Please review. Thanks, Felicia -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.xiph.org/pipermail/opus/attachments/20170131/9a912bb4/attachment-0001.html> -------------- next part -------------- A non-text attachment was scrubbed... Name:

Thank Jean-Marc! The speedup percentages are all relative to the entire encoder. Comparing to master, this optimization patch speeds up fixed-point SILK encoder on NEON as following: Complexity 5: 6.1% Complexity 6: 5.8% Complexity 8: 5.5% Complexity 10: 4.0% when testing on an Acer Chromebook, ARMv7 Processor rev 3 (v7l), CPU max MHz: 2116.5 Thanks, Linfeng On Wed, Apr 5, 2017 at 11:02 AM,

Hello R-friends, i have a question to the "text"-function. a little test-dataset for better understanding: -the dataset was imported with read.table(....,header=TRUE) s1-s10 are the samplenames var1 var2 var3 s1 1 1 2 s2 2 3 1 s3 2 2 3 s4 5 4 3 s5 4 2 3 s6 6 3 2 s7 8 5 4 s8 7 2 1 s9 9 3 2

Hi, I am trying to read the "credit.lisp" file of the Japanese credit database in UCI repository, but it is in lisp format which I do not know how to read. I have not found how to do that in the foreign library http://archive.ics.uci.edu/ml/datasets/Japanese+Credit+Screening <http://archive.ics.uci.edu/ml/datasets/Japanese+Credit+Screening> Could anyone help me? Best

Hi, ABCRegister.td : def SGPR32 : RegisterClass<"ABC", [i32], 16, (add S0, S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 )>; def SFGPR32 : RegisterClass<"ABC", [f32], 16, (add S0, S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15 )>; ===== Instruction selection ends: ... t8: i32 = ADDrr t37, t32

Hi R-users, I have a question relating to the HoltWinters() function. I am trying to forecast a series using the Holt Winters methodology but I am getting some unusual results. I had previously been using R for Windows version 2.7.2 and have just started using R 2.9.1. While using version 2.7.2 I was getting reasonable results however upon changing versions I found I started to see unusual

I am confused when trying the function survfit. my question is: what does the survival curve given by plot.survfit mean? is it the survival curve with different covariates at different points? or just the baseline survival curve? for example, I run the following code and get the survival curve #### library(survival) fit<-coxph(Surv(futime,fustat)~resid.ds+rx+ecog.ps,data=ovarian)

Hi, I need to subset different levels of vector in a dataset to create a new dataframe that contains only these. These observations are not numerical, so I can't use the subset() function (at least this is the response I get from R). Suppose the dataframe looks like this:   ParticipID    ERP   Electrode 1         s1  0.0370       FP1 2         s2 35.0654       FP2 3         s3

Dear R users, I have copied for following table from an article on "Using confidence intervals in within-subject designs": Subject 1sec 2sec 5sec 1 10 13 13 12.00 2 6 8 8 7.33 3 11 14 14 13.00 4 22 23 25 23.33 5 16 18 20 18.00 6 15 17 17 16.33 7 1 1 4 2.00 8 12 15 17 14.67 9 9 12 12 11.00 10 8 9 12 9.67 I rearranged the data this way:

I have a multidimensional data which looks like the following: "S1-a" "S2-b" "S3-c" "S4-d" "S5-a" "S6-b" "S7-c" "S8-d" "T1-A" "T1-B" "T1-C" "T1-D" "T2-A" "T2-B" "T2-C" "T2-D" I read it from csv file and would like to have 16

Why the output is different for this below program when compiled using clang with fast-math optimization #include<stdio.h> int main() { double d = 1.0; double max = 1.79769e+308; d /= max; printf("d:%e:\n", d); d *= max; printf("d:%e:\n", d); return 0; } prints 0 with fast math but 1 without fast math. -------------- next part -------------- An

I don't know if this is the best place to post this question but I will try anyway. I have two experiements for which I use one-way matched-randomized ANOVA for the analysis and I would like to compare different treatments in the two experiments. The only common group in the two experiments are the controls. Is there any ANOVA design that allows me to make this comparison taking into