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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 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(

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

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,

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

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

I have a multilevel dataframe (df): ID Date Segment Slice Tract Lesion 1 CSPP005 12/4/2007 1 1 LCST 0 2 CSPP005 12/4/2007 1 1 LPC 2 3 CSPP005 12/4/2007 1 1 RPC 3 4 CSPP005 12/4/2007 1 1 RCST 1 5 CSPP005 12/4/2007 1 1 LGM 0 6 CSPP005 12/4/2007 1 1

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 Linfeng, On 06/02/17 02:51 PM, Linfeng Zhang wrote: > However, the critical thing is that all the states in each stage when > processing input[i] are reused by the next input[i+1]. That is > input[i+1] must wait input[i] for 1 stage, and input[i+2] must wait > input[i+1] for 1 stage, etc. That is indeed the tricky part... and the one I think you could do slightly differently. If

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 complexity parameter. > > It's hard to get a universal function to handle all these orders > efficiently. Any suggestions? I can think of two ways of handling larger orders. The obvious one is

Hi Jean-Marc, Attached is the silk_warped_autocorrelation_FIX_neon() which implements your idea. Speed improvement vs the previous optimization: Complexity 0-4: Doesn't call this function. Complexity 5: 2.1% (order = 16) Complexity 6: 1.0% (order = 20) Complexity 8: 0.1% (order = 24) Complexity 10: 0.1% (order = 24) Code size of silk_warped_autocorrelation_FIX_neon() changes from 2,644

Hi Linfeng, Thanks for the updated patch. I'll have a look and get back to you. When you report speedup percentages, is that relative to the entire encoder or relative to just that function in C? Also, what's the speedup compared to master? Cheers, Jean-Marc On 05/04/17 12:14 PM, Linfeng Zhang wrote: > I attached a new patch with small cleanup (disassembly is identical as > the

Change the assembly code to use only relative references of symbols for the kernel to be PIE compatible. Position Independent Executable (PIE) support will allow to extended the KASLR randomization range below the -2G memory limit. Signed-off-by: Thomas Garnier <thgarnie at google.com> --- arch/x86/crypto/aes-x86_64-asm_64.S | 45 ++++++++----- arch/x86/crypto/aesni-intel_asm.S

Hi Linfeng, I had a closer look at your patch and the code looks good -- and slightly simpler than I had anticipated, so that's good. I did some profiling on a Cortex A57 and I've been seeing slightly less improvement than you're reporting, more like 3.5% at complexity 8. It appears that the warped autocorrelation function itself is only faster by a factor of about 1.35. That's a

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:

Hi, I want to encode Loongson ISA initially https://gist.github.com/xiangzhai/8ae6966e2f02a94e180dd16ff1cd60ac gslbx           $2,0($3,$4) It is equivalent to: dadd $1, $3, $4 lb $2,0($1) I just use  mem_simmptr  as the default value of  DAGOperand MO , because  MipsMemAsmOperand  use  parseMemOperand  to parse general  MemOffset  and only *one*  AnyRegister , for example: 0($1) But 

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

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

From: Dejan Latinovic <Dejan.Latinovic at imgtec.com> --- usr/include/arch/mips64/klibc/archconfig.h | 3 + usr/include/arch/mips64/klibc/archsetjmp.h | 39 ++++++ usr/include/arch/mips64/machine/asm.h | 76 ++++++++++ usr/include/fcntl.h | 2 +- usr/include/sys/md.h | 1 + usr/include/sys/resource.h | 4 +-