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• approximating log10[x^k0 + k1]

  - by Yale Zhang

  Greetings. I'm trying to approximate the function Log10[x^k0 + k1], where .21 < k0 < 21, 0 < k1 < ~2000, and x is integer < 2^14. k0 & k1 are constant. For practical purposes, you can assume k0 = 2.12, k1 = 2660. The desired accuracy is 5*10^-4 relative error. This function is virtually identical to Log[x], except near 0, where it differs a lot. I already have came up with a SIMD implementation that is ~1.15x faster than a simple lookup table, but would like to improve it if possible, which I think is very hard due to lack of efficient instructions. My SIMD implementation uses 16bit fixed point arithmetic to evaluate a 3rd degree polynomial (I use least squares fit). The polynomial uses different coefficients for different input ranges. There are 8 ranges, and range i spans (64)2^i to (64)2^(i + 1). The rational behind this is the derivatives of Log[x] drop rapidly with x, meaning a polynomial will fit it more accurately since polynomials are an exact fit for functions that have a derivative of 0 beyond a certain order. SIMD table lookups are done very efficiently with a single _mm_shuffle_epi8(). I use SSE's float to int conversion to get the exponent and significand used for the fixed point approximation. I also software pipelined the loop to get ~1.25x speedup, so further code optimizations are probably unlikely. What I'm asking is if there's a more efficient approximation at a higher level? For example: Can this function be decomposed into functions with a limited domain like log2((2^x) * significand) = x + log2(significand) hence eliminating the need to deal with different ranges (table lookups). The main problem I think is adding the k1 term kills all those nice log properties that we know and love, making it not possible. Or is it? Iterative method? don't think so because the Newton method for log[x] is already a complicated expression Exploiting locality of neighboring pixels? - if the range of the 8 inputs fall in the same approximation range, then I can look up a single coefficient, instead of looking up separate coefficients for each element. Thus, I can use this as a fast common case, and use a slower, general code path when it isn't. But for my data, the range needs to be ~2000 before this property hold 70% of the time, which doesn't seem to make this method competitive. Please, give me some opinion, especially if you're an applied mathematician, even if you say it can't be done. Thanks.

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• How can I strip Python logging calls without commenting them out?

  - by cdleary

  Today I was thinking about a Python project I wrote about a year back where I used logging pretty extensively. I remember having to comment out a lot of logging calls in inner-loop-like scenarios (the 90% code) because of the overhead (hotshot indicated it was one of my biggest bottlenecks). I wonder now if there's some canonical way to programmatically strip out logging calls in Python applications without commenting and uncommenting all the time. I'd think you could use inspection/recompilation or bytecode manipulation to do something like this and target only the code objects that are causing bottlenecks. This way, you could add a manipulator as a post-compilation step and use a centralized configuration file, like so: [Leave ERROR and above] my_module.SomeClass.method_with_lots_of_warn_calls [Leave WARN and above] my_module.SomeOtherClass.method_with_lots_of_info_calls [Leave INFO and above] my_module.SomeWeirdClass.method_with_lots_of_debug_calls Of course, you'd want to use it sparingly and probably with per-function granularity -- only for code objects that have shown logging to be a bottleneck. Anybody know of anything like this? Note: There are a few things that make this more difficult to do in a performant manner because of dynamic typing and late binding. For example, any calls to a method named debug may have to be wrapped with an if not isinstance(log, Logger). In any case, I'm assuming all of the minor details can be overcome, either by a gentleman's agreement or some run-time checking. :-)

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• c++ optimize array of ints

  - by a432511

  I have a 2D lookup table of int16_t. int16_t my_array[37][73] = {{**DATA HERE**}} I have a mixture of values that range from just above the range of int8_t to just below the range of int8_t and some of the values repeat themselves. I am trying to reduce the size of this lookup table. What I have done so far is split each int16_t value into two int8_t values to visualize the wasted bytes. int8_t part_1 = original_value >> 4; int8_t part_2 = original_value & 0x0000FFFF; // If the upper 4 bits of the original_value were empty if(part_1 == 0) wasted_bytes_count++; I can easily remove the zero value int8_t that are wasting a byte of space and I can also remove the duplicate values, but my question is how do I do remove those values while retaining the ability to lookup based on the two indices? I contemplated translating this into a 1D array and adding a number following each duplicated value that would represent the number of duplicates that were removed, but I am struggling with how I would then identify what is a lookup value and what is a duplicate count. Also, it is further complicated by stripping out the zero int8_t values that were wasted bytes. EDIT: This array is stored in ROM already. RAM is even more limited than ROM so it is already stored in ROM. EDIT: I am going to post a bounty for this question as soon as I can. I need a complete answer of how to store the information AND retrieve it. It does not need to be a 2D array as long as I can get the same values. EDIT: Adding the actual array below: {150,145,140,135,130,125,120,115,110,105,100,95,90,85,80,75,70,65,60,55,50,45,40,35,30,25,20,15,10,5,0,-4,-9,-14,-19,-24,-29,-34,-39,-44,-49,-54,-59,-64,-69,-74,-79,-84,-89,-94,-99,104,109,114,119,124,129,134,139,144,149,154,159,164,169,174,179,175,170,165,160,155,150}, \ {143,137,131,126,120,115,110,105,100,95,90,85,80,75,71,66,62,57,53,48,44,39,35,31,27,22,18,14,9,5,1,-3,-7,-11,-16,-20,-25,-29,-34,-38,-43,-47,-52,-57,-61,-66,-71,-76,-81,-86,-91,-96,101,107,112,117,123,128,134,140,146,151,157,163,169,175,178,172,166,160,154,148,143}, \ {130,124,118,112,107,101,96,92,87,82,78,74,70,65,61,57,54,50,46,42,38,34,31,27,23,19,16,12,8,4,1,-2,-6,-10,-14,-18,-22,-26,-30,-34,-38,-43,-47,-51,-56,-61,-65,-70,-75,-79,-84,-89,-94,100,105,111,116,122,128,135,141,148,155,162,170,177,174,166,159,151,144,137,130}, \ {111,104,99,94,89,85,81,77,73,70,66,63,60,56,53,50,46,43,40,36,33,30,26,23,20,16,13,10,6,3,0,-3,-6,-9,-13,-16,-20,-24,-28,-32,-36,-40,-44,-48,-52,-57,-61,-65,-70,-74,-79,-84,-88,-93,-98,103,109,115,121,128,135,143,152,162,172,176,165,154,144,134,125,118,111}, \ {85,81,77,74,71,68,65,63,60,58,56,53,51,49,46,43,41,38,35,32,29,26,23,19,16,13,10,7,4,1,-1,-3,-6,-9,-13,-16,-19,-23,-26,-30,-34,-38,-42,-46,-50,-54,-58,-62,-66,-70,-74,-78,-83,-87,-91,-95,100,105,110,117,124,133,144,159,178,160,141,125,112,103,96,90,85}, \ {62,60,58,57,55,54,52,51,50,48,47,46,44,42,41,39,36,34,31,28,25,22,19,16,13,10,7,4,2,0,-3,-5,-8,-10,-13,-16,-19,-22,-26,-29,-33,-37,-41,-45,-49,-53,-56,-60,-64,-67,-70,-74,-77,-80,-83,-86,-89,-91,-94,-97,101,105,111,130,109,84,77,74,71,68,66,64,62}, \ {46,46,45,44,44,43,42,42,41,41,40,39,38,37,36,35,33,31,28,26,23,20,16,13,10,7,4,1,-1,-3,-5,-7,-9,-12,-14,-16,-19,-22,-26,-29,-33,-36,-40,-44,-48,-51,-55,-58,-61,-64,-66,-68,-71,-72,-74,-74,-75,-74,-72,-68,-61,-48,-25,2,22,33,40,43,45,46,47,46,46}, \ {36,36,36,36,36,35,35,35,35,34,34,34,34,33,32,31,30,28,26,23,20,17,14,10,6,3,0,-2,-4,-7,-9,-10,-12,-14,-15,-17,-20,-23,-26,-29,-32,-36,-40,-43,-47,-50,-53,-56,-58,-60,-62,-63,-64,-64,-63,-62,-59,-55,-49,-41,-30,-17,-4,6,15,22,27,31,33,34,35,36,36}, \ {30,30,30,30,30,30,30,29,29,29,29,29,29,29,29,28,27,26,24,21,18,15,11,7,3,0,-3,-6,-9,-11,-12,-14,-15,-16,-17,-19,-21,-23,-26,-29,-32,-35,-39,-42,-45,-48,-51,-53,-55,-56,-57,-57,-56,-55,-53,-49,-44,-38,-31,-23,-14,-6,0,7,13,17,21,24,26,27,29,29,30}, \ {25,25,26,26,26,25,25,25,25,25,25,25,25,26,25,25,24,23,21,19,16,12,8,4,0,-3,-7,-10,-13,-15,-16,-17,-18,-19,-20,-21,-22,-23,-25,-28,-31,-34,-37,-40,-43,-46,-48,-49,-50,-51,-51,-50,-48,-45,-42,-37,-32,-26,-19,-13,-7,-1,3,7,11,14,17,19,21,23,24,25,25}, \ {21,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,21,20,18,16,13,9,5,1,-3,-7,-11,-14,-17,-18,-20,-21,-21,-22,-22,-22,-23,-23,-25,-27,-29,-32,-35,-37,-40,-42,-44,-45,-45,-45,-44,-42,-40,-36,-32,-27,-22,-17,-12,-7,-3,0,3,7,9,12,14,16,18,19,20,21,21}, \ {18,19,19,19,19,19,19,19,19,19,19,19,19,19,19,19,18,17,16,14,10,7,2,-1,-6,-10,-14,-17,-19,-21,-22,-23,-24,-24,-24,-24,-23,-23,-23,-24,-26,-28,-30,-33,-35,-37,-38,-39,-39,-38,-36,-34,-31,-28,-24,-19,-15,-10,-6,-3,0,1,4,6,8,10,12,14,15,16,17,18,18}, \ {16,16,17,17,17,17,17,17,17,17,17,16,16,16,16,16,16,15,13,11,8,4,0,-4,-9,-13,-16,-19,-21,-23,-24,-25,-25,-25,-25,-24,-23,-21,-20,-20,-21,-22,-24,-26,-28,-30,-31,-32,-31,-30,-29,-27,-24,-21,-17,-13,-9,-6,-3,-1,0,2,4,5,7,9,10,12,13,14,15,16,16}, \ {14,14,14,15,15,15,15,15,15,15,14,14,14,14,14,14,13,12,11,9,5,2,-2,-6,-11,-15,-18,-21,-23,-24,-25,-25,-25,-25,-24,-22,-21,-18,-16,-15,-15,-15,-17,-19,-21,-22,-24,-24,-24,-23,-22,-20,-18,-15,-12,-9,-5,-3,-1,0,1,2,4,5,6,8,9,10,11,12,13,14,14}, \ {12,13,13,13,13,13,13,13,13,13,13,13,12,12,12,12,11,10,9,6,3,0,-4,-8,-12,-16,-19,-21,-23,-24,-24,-24,-24,-23,-22,-20,-17,-15,-12,-10,-9,-9,-10,-12,-13,-15,-17,-17,-18,-17,-16,-15,-13,-11,-8,-5,-3,-1,0,1,1,2,3,4,6,7,8,9,10,11,12,12,12}, \ {11,11,11,11,11,12,12,12,12,12,11,11,11,11,11,10,10,9,7,5,2,-1,-5,-9,-13,-17,-20,-22,-23,-23,-23,-23,-22,-20,-18,-16,-14,-11,-9,-6,-5,-4,-5,-6,-8,-9,-11,-12,-12,-12,-12,-11,-9,-8,-6,-3,-1,0,0,1,1,2,3,4,5,6,7,8,9,10,11,11,11}, \ {10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,7,6,3,0,-3,-6,-10,-14,-17,-20,-21,-22,-22,-22,-21,-19,-17,-15,-13,-10,-8,-6,-4,-2,-2,-2,-2,-4,-5,-7,-8,-8,-9,-8,-8,-7,-5,-4,-2,0,0,1,1,1,2,2,3,4,5,6,7,8,9,10,10,10}, \ {9,9,9,9,9,9,9,10,10,9,9,9,9,9,9,8,8,6,5,2,0,-4,-7,-11,-15,-17,-19,-21,-21,-21,-20,-18,-16,-14,-12,-10,-8,-6,-4,-2,-1,0,0,0,-1,-2,-4,-5,-5,-6,-6,-5,-5,-4,-3,-1,0,0,1,1,1,1,2,3,3,5,6,7,8,8,9,9,9}, \ {9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,7,5,4,1,-1,-5,-8,-12,-15,-17,-19,-20,-20,-19,-18,-16,-14,-11,-9,-7,-5,-4,-2,-1,0,0,1,1,0,0,-2,-3,-3,-4,-4,-4,-3,-3,-2,-1,0,0,0,0,0,1,1,2,3,4,5,6,7,8,8,9,9}, \ {9,9,9,8,8,8,9,9,9,9,9,8,8,8,8,7,6,5,3,0,-2,-5,-9,-12,-15,-17,-18,-19,-19,-18,-16,-14,-12,-9,-7,-5,-4,-2,-1,0,0,1,1,1,1,0,0,-1,-2,-2,-3,-3,-2,-2,-1,-1,0,0,0,0,0,0,0,1,2,3,4,5,6,7,8,8,9}, \ {8,8,8,8,8,8,9,9,9,9,9,9,8,8,8,7,6,4,2,0,-3,-6,-9,-12,-15,-17,-18,-18,-17,-16,-14,-12,-10,-8,-6,-4,-2,-1,0,0,1,2,2,2,2,1,0,0,-1,-1,-1,-2,-2,-1,-1,0,0,0,0,0,0,0,0,0,1,2,3,4,5,6,7,8,8}, \ {8,8,8,8,9,9,9,9,9,9,9,9,9,8,8,7,5,3,1,-1,-4,-7,-10,-13,-15,-16,-17,-17,-16,-15,-13,-11,-9,-6,-5,-3,-2,0,0,0,1,2,2,2,2,1,1,0,0,0,-1,-1,-1,-1,-1,0,0,0,0,-1,-1,-1,-1,-1,0,0,1,3,4,5,7,7,8}, \ {8,8,9,9,9,9,10,10,10,10,10,10,10,9,8,7,5,3,0,-2,-5,-8,-11,-13,-15,-16,-16,-16,-15,-13,-12,-10,-8,-6,-4,-2,-1,0,0,1,2,2,3,3,2,2,1,0,0,0,0,0,0,0,0,0,0,-1,-1,-2,-2,-2,-2,-2,-1,0,0,1,3,4,6,7,8}, \ {7,8,9,9,9,10,10,11,11,11,11,11,10,10,9,7,5,3,0,-2,-6,-9,-11,-13,-15,-16,-16,-15,-14,-13,-11,-9,-7,-5,-3,-2,0,0,1,1,2,3,3,3,3,2,2,1,1,0,0,0,0,0,0,0,-1,-1,-2,-3,-3,-4,-4,-4,-3,-2,-1,0,1,3,5,6,7}, \ {6,8,9,9,10,11,11,12,12,12,12,12,11,11,9,7,5,2,0,-3,-7,-10,-12,-14,-15,-16,-15,-15,-13,-12,-10,-8,-7,-5,-3,-1,0,0,1,2,2,3,3,4,3,3,3,2,2,1,1,1,0,0,0,0,-1,-2,-3,-4,-4,-5,-5,-5,-5,-4,-2,-1,0,2,3,5,6}, \ {6,7,8,10,11,12,12,13,13,14,14,13,13,11,10,8,5,2,0,-4,-8,-11,-13,-15,-16,-16,-16,-15,-13,-12,-10,-8,-6,-5,-3,-1,0,0,1,2,3,3,4,4,4,4,4,3,3,3,2,2,1,1,0,0,-1,-2,-3,-5,-6,-7,-7,-7,-6,-5,-4,-3,-1,0,2,4,6}, \ {5,7,8,10,11,12,13,14,15,15,15,14,14,12,11,8,5,2,-1,-5,-9,-12,-14,-16,-17,-17,-16,-15,-14,-12,-11,-9,-7,-5,-3,-1,0,0,1,2,3,4,4,5,5,5,5,5,5,4,4,3,3,2,1,0,-1,-2,-4,-6,-7,-8,-8,-8,-8,-7,-6,-4,-2,0,1,3,5}, \ {4,6,8,10,12,13,14,15,16,16,16,16,15,13,11,9,5,2,-2,-6,-10,-13,-16,-17,-18,-18,-17,-16,-15,-13,-11,-9,-7,-5,-4,-2,0,0,1,3,3,4,5,6,6,7,7,7,7,7,6,5,4,3,2,0,-1,-3,-5,-7,-8,-9,-10,-10,-10,-9,-7,-5,-4,-1,0,2,4}, \ {4,6,8,10,12,14,15,16,17,18,18,17,16,15,12,9,5,1,-3,-8,-12,-15,-18,-19,-20,-20,-19,-18,-16,-15,-13,-11,-8,-6,-4,-2,-1,0,1,3,4,5,6,7,8,9,9,9,9,9,9,8,7,5,3,1,-1,-3,-6,-8,-10,-11,-12,-12,-11,-10,-9,-7,-5,-2,0,1,4}, \ {4,6,8,11,13,15,16,18,19,19,19,19,18,16,13,10,5,0,-5,-10,-15,-18,-21,-22,-23,-22,-22,-20,-18,-17,-14,-12,-10,-8,-5,-3,-1,0,1,3,5,6,8,9,10,11,12,12,13,12,12,11,9,7,5,2,0,-3,-6,-9,-11,-12,-13,-13,-12,-11,-10,-8,-6,-3,-1,1,4}, \ {3,6,9,11,14,16,17,19,20,21,21,21,19,17,14,10,4,-1,-8,-14,-19,-22,-25,-26,-26,-26,-25,-23,-21,-19,-17,-14,-12,-9,-7,-4,-2,0,1,3,5,7,9,11,13,14,15,16,16,16,16,15,13,10,7,4,0,-3,-7,-10,-12,-14,-15,-14,-14,-12,-11,-9,-6,-4,-1,1,3}, \ {4,6,9,12,14,17,19,21,22,23,23,23,21,19,15,9,2,-5,-13,-20,-25,-28,-30,-31,-31,-30,-29,-27,-25,-22,-20,-17,-14,-11,-9,-6,-3,0,1,4,6,9,11,13,15,17,19,20,21,21,21,20,18,15,11,6,2,-2,-7,-11,-13,-15,-16,-16,-15,-13,-11,-9,-7,-4,-1,1,4}, \ {4,7,10,13,15,18,20,22,24,25,25,25,23,20,15,7,-2,-12,-22,-29,-34,-37,-38,-38,-37,-36,-34,-31,-29,-26,-23,-20,-17,-13,-10,-7,-4,-1,2,5,8,11,13,16,18,21,23,24,26,26,26,26,24,21,17,12,5,0,-6,-10,-14,-16,-16,-16,-15,-14,-12,-10,-7,-4,-1,1,4}, \ {4,7,10,13,16,19,22,24,26,27,27,26,24,19,11,-1,-15,-28,-37,-43,-46,-47,-47,-45,-44,-41,-39,-36,-32,-29,-26,-22,-19,-15,-11,-8,-4,-1,2,5,9,12,15,19,22,24,27,29,31,33,33,33,32,30,26,21,14,6,0,-6,-11,-14,-15,-16,-15,-14,-12,-9,-7,-4,-1,1,4}, \ {6,9,12,15,18,21,23,25,27,28,27,24,17,4,-14,-34,-49,-56,-60,-60,-60,-58,-56,-53,-50,-47,-43,-40,-36,-32,-28,-25,-21,-17,-13,-9,-5,-1,2,6,10,14,17,21,24,28,31,34,37,39,41,42,43,43,41,38,33,25,17,8,0,-4,-8,-10,-10,-10,-8,-7,-4,-2,0,3,6}, \ {22,24,26,28,30,32,33,31,23,-18,-81,-96,-99,-98,-95,-93,-89,-86,-82,-78,-74,-70,-66,-62,-57,-53,-49,-44,-40,-36,-32,-27,-23,-19,-14,-10,-6,-1,2,6,10,15,19,23,27,31,35,38,42,45,49,52,55,57,60,61,63,63,62,61,57,53,47,40,33,28,23,21,19,19,19,20,22}, \ {168,173,178,176,171,166,161,156,151,146,141,136,131,126,121,116,111,106,101,-96,-91,-86,-81,-76,-71,-66,-61,-56,-51,-46,-41,-36,-31,-26,-21,-16,-11,-6,-1,3,8,13,18,23,28,33,38,43,48,53,58,63,68,73,78,83,88,93,98,103,108,113,118,123,128,133,138,143,148,153,158,163,168}, \ Thanks for your time.

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• Quickest way to write to file in java

  - by user1097772

  I'm writing an application which compares directory structure. First I wrote an application which writes gets info about files - one line about each file or directory. My soulution is: calling method toFile Static PrintWriter pw = new PrintWriter(new BufferedWriter( new FileWriter("DirStructure.dlis")), true); String line; // info about file or directory public void toFile(String line) { pw.println(line); } and of course pw.close(), at the end. My question is, can I do it quicker? What is the quickest way? Edit: quickest way = quickest writing in the file

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• when is java faster than c++ (or when is JIT faster then precompiled)?

  - by kostja

  I have heard that under certain circumstances, Java programs or rather parts of java programs are able to be executed faster than the "same" code in C++ (or other precompiled code) due to JIT optimizations. This is due to the compiler being able to determine the scope of some variables, avoid some conditionals and pull similar tricks at runtime. Could you give an (or better - some) example, where this applies? And maybe outline the exact conditions under which the compiler is able to optimize the bytecode beyond what is possible with precompiled code? NOTE : This question is not about comparing Java to C++. Its about the possibilities of JIT compiling. Please no flaming. I am also not aware of any duplicates. Please point them out if you are.

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• Google Web Optimizer -- How long until winning combination?

  - by Django Reinhardt

  I've had an A/B Test running in Google Web Optimizer for six weeks now, and there's still no end in sight. Google is still saying: "We have not gathered enough data yet to show any significant results. When we collect more data we should be able to show you a winning combination." Is there any way of telling how close Google is to making up its mind? (Does anyone know what algorithm does it use to decide if there's been any "high confidence winners"?) According to the Google help documentation: Sometimes we simply need more data to be able to reach a level of high confidence. A tested combination typically needs around 200 conversions for us to judge its performance with certainty. But all of our conversions have over 200 conversations at the moment: 230 / 4061 (Original) 223 / 3937 (Variation 1) 205 / 3984 (Variation 2) 205 / 4007 (Variation 3) How much longer is it going to have to run?? Thanks for any help.

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• Is opening too many datacontexts bad?

  - by ryudice

  I've been checking my application with linq 2 sql profiler, and I noticed that it opens a lot of datacontexts, most of them are opened by the linq datasource I used, since my repositories use only the instance stored in Request.Items, is it bad to open too many datacontext? and how can I make my linqdatasource to use the datacontext that I store in Request.Items for the duration of the request? thanks for any help!

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• Is there a faster TList implementation ?

  - by dmauric.mp

  My application makes heavy use of TList, so I was wondering if there are any alternative implementations that are faster or optimized for particular use case. I know of RtlVCLOptimize.pas 2.77, which has optimized implementations of several TList methods. But I'd like to know if there is anything else out there. I also don't require it to be a TList descendant, I just need the TList functionality regardless of how it's implemented. It's entirely possible, given the rather basic functionality TList provides, that there is not much room for improvement, but would still like to verify that, hence this question.

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• Data Access from single table in sql server 2005 is too slow

  - by Muhammad Kashif Nadeem

  Following is the script of table. Accessing data from this table is too slow. SET ANSI_NULLS ON GO SET QUOTED_IDENTIFIER ON GO CREATE TABLE [dbo].[Emails]( [id] [int] IDENTITY(1,1) NOT NULL, [datecreated] [datetime] NULL CONSTRAINT [DF_Emails_datecreated] DEFAULT (getdate()), [UID] [nvarchar](250) COLLATE Latin1_General_CI_AS NULL, [From] [nvarchar](100) COLLATE Latin1_General_CI_AS NULL, [To] [nvarchar](100) COLLATE Latin1_General_CI_AS NULL, [Subject] [nvarchar](max) COLLATE Latin1_General_CI_AS NULL, [Body] [nvarchar](max) COLLATE Latin1_General_CI_AS NULL, [HTML] [nvarchar](max) COLLATE Latin1_General_CI_AS NULL, [AttachmentCount] [int] NULL, [Dated] [datetime] NULL ) ON [PRIMARY] Following query takes 50 seconds to fetch data. select id, datecreated, UID, [From], [To], Subject, AttachmentCount, Dated from emails If I include Body and Html in select then time is event worse. indexes are on: id unique clustered From Non unique non clustered To Non unique non clustered Tabls has currently 180000+ records. There might be 100,000 records each month so this will become more slow as time will pass. Does splitting data into two table will solve the problem? What other indexes should be there?

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• how to speed up the code??

  - by kaushik

  in my program i have a method which requires about 4 files to be open each time it is called,as i require to take some data.all this data from the file i have been storing in list for manupalation. I approximatily need to call this method about 10,000 times.which is making my program very slow? any method for handling this files in a better ways and is storing the whole data in list time consuming what is better alternatives for list? I can give some code,but my previous question was closed as that only confused everyone as it is a part of big program and need to be explained completely to understand,so i am not giving any code,please suggest ways thinking this as a general question... thanks in advance

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• Code runs 6 times slower with 2 threads than with 1

  - by Edward Bird

  So I have written some code to experiment with threads and do some testing. The code should create some numbers and then find the mean of those numbers. I think it is just easier to show you what I have so far. I was expecting with two threads that the code would run about 2 times as fast. Measuring it with a stopwatch I think it runs about 6 times slower! void findmean(std::vector<double>*, std::size_t, std::size_t, double*); int main(int argn, char** argv) { // Program entry point std::cout << "Generating data..." << std::endl; // Create a vector containing many variables std::vector<double> data; for(uint32_t i = 1; i <= 1024 * 1024 * 128; i ++) data.push_back(i); // Calculate mean using 1 core double mean = 0; std::cout << "Calculating mean, 1 Thread..." << std::endl; findmean(&data, 0, data.size(), &mean); mean /= (double)data.size(); // Print result std::cout << " Mean=" << mean << std::endl; // Repeat, using two threads std::vector<std::thread> thread; std::vector<double> result; result.push_back(0.0); result.push_back(0.0); std::cout << "Calculating mean, 2 Threads..." << std::endl; // Run threads uint32_t halfsize = data.size() / 2; uint32_t A = 0; uint32_t B, C, D; // Split the data into two blocks if(data.size() % 2 == 0) { B = C = D = halfsize; } else if(data.size() % 2 == 1) { B = C = halfsize; D = hsz + 1; } // Run with two threads thread.push_back(std::thread(findmean, &data, A, B, &(result[0]))); thread.push_back(std::thread(findmean, &data, C, D , &(result[1]))); // Join threads thread[0].join(); thread[1].join(); // Calculate result mean = result[0] + result[1]; mean /= (double)data.size(); // Print result std::cout << " Mean=" << mean << std::endl; // Return return EXIT_SUCCESS; } void findmean(std::vector<double>* datavec, std::size_t start, std::size_t length, double* result) { for(uint32_t i = 0; i < length; i ++) { *result += (*datavec).at(start + i); } } I don't think this code is exactly wonderful, if you could suggest ways of improving it then I would be grateful for that also.

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• most efficient method of turning multiple 1D arrays into columns of a 2D array

  - by Ty W

  As I was writing a for loop earlier today, I thought that there must be a neater way of doing this... so I figured I'd ask. I looked briefly for a duplicate question but didn't see anything obvious. The Problem: Given N arrays of length M, turn them into a M-row by N-column 2D array Example: $id = [1,5,2,8,6] $name = [a,b,c,d,e] $result = [[1,a], [5,b], [2,c], [8,d], [6,e]] My Solution: Pretty straight forward and probably not optimal, but it does work: <?php // $row is returned from a DB query // $row['<var>'] is a comma separated string of values $categories = array(); $ids = explode(",", $row['ids']); $names = explode(",", $row['names']); $titles = explode(",", $row['titles']); for($i = 0; $i < count($ids); $i++) { $categories[] = array("id" => $ids[$i], "name" => $names[$i], "title" => $titles[$i]); } ?> note: I didn't put the name = value bit in the spec, but it'd be awesome if there was some way to keep that as well.

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• Does replacing statements by expressions using the C++ comma operator could allow more compiler opti

  - by Gabriel Cuvillier

  The C++ comma operator is used to chain individual expressions, yielding the value of the last executed expression as the result. For example the skeleton code (6 statements, 6 expressions): step1; step2; if (condition) step3; return step4; else return step5; May be rewritten to: (1 statement, 6 expressions) return step1, step2, condition? step3, step4 : step5; I noticed that it is not possible to perform step-by-step debugging of such code, as the expression chain seems to be executed as a whole. Does it means that the compiler is able to perform special optimizations which are not possible with the traditional statement approach (specially if the steps are const or inline)? Note: I'm not talking about the coding style merit of that way of expressing sequence of expressions! Just about the possible optimisations allowed by replacing statements by expressions.

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• Why is doing a top(1) on an indexed column in SQL Server slow?

  - by reinier

  I'm puzzled by the following. I have a DB with around 10 million rows, and (among other indices) on 1 column (campaignid_int) is an index. Now I have 700k rows where the campaignid is indeed 3835 For all these rows, the connectionid is the same. I just want to find out this connectionid. use messaging_db; SELECT TOP (1) connectionid FROM outgoing_messages WITH (NOLOCK) WHERE (campaignid_int = 3835) Now this query takes approx 30 seconds to perform! I (with my small db knowledge) would expect that it would take any of the rows, and return me that connectionid If I test this same query for a campaign which only has 1 entry, it goes really fast. So the index works. How would I tackle this and why does this not work? edit: estimated execution plan: select (0%) - top (0%) - clustered index scan (100%)

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• Fastest way to convert string to integer in PHP

  - by nickf

  Using PHP, what's the fastest way to convert a string like this: "123" to an integer? Why is that particular method the fastest? What happens if it gets unexpected input, such as "hello" or an array?

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• Alternate User select interface in django admin to reduce page size on large site?

  - by David Eyk

  I have a Django-based site with roughly 300,000 User objects. Admin pages for objects with a ForeignKey field to User take a very long time to load as the resulting form is about 6MB in size. Of course, the resulting dropdown isn't particularly useful, either. Are there any off-the-shelf replacements for handling this case? I've been googling for a snippet or a blog entry, but haven't found anything yet. I'd like to have a smaller download size and a more usable interface.

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• I'm on a 2MBPS internet connection but i want to check a website like i'm on a 256 KBPS connection o

  - by metal-gear-solid

  Does anyone know how to test? I'm on Windows XP? and Firefox , IE7 installed

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• Database structure, optimizing table with lots of rows

  - by aepheus

  I have a table with a lot of rows. The structure is something like this: UserID | ItemID | Item Data... Would I see any gains in query time if I separated this into a table per user, or per smaller group of users? Queries are always single user getting/modifying a selection of items.

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• Drawbacks of Dynamic Query in Sqlserver 2005 ?

  - by KuldipMCA

  I have using the many dynamic Query in my database for the procedures because my filter is not fix so i have taken @filter as parameter and pass in the procedure. Declare @query as varchar(8000) Declare @Filter as varchar(1000) set @query = 'Select * from Person.Address where 1=1 and ' + @Filter exec(@query) Like that my filter contain any Field from the table for comparison. It will affect my performance or not ? is there any alternate way to achieve this type of things

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• Optimizing an iphone app for 3G in landscape with opengl, camera, quartz

  - by Joey

  I have an iphone app that basically uses the camera, an opengl layer, and UIViews (some drawing with Quartz). It runs ok on 3GS, but on the 3G it is unusable. Particularly, when I press a UIButton, it literally takes sometimes 10 seconds to register the press. Shark doesn't do me much good because it crashes when I try to profile even a tiny portion, and I've tried turning off some of the layers to see if they might be obvious contributors to the lag. I've noticed that turning off the camera really helps. I'm wondering if anyone has any familiarity with this and might suggest some likely causes. I had issues with extreme slowdown from running my app in landscape mode and using transforms, so considered that might be a cause, but I'm wondering if hoping for a 3G to run something with all of the above elements is just not really possible considering the camera seems to really cost a lot. The fact that the buttons are horribly delayed in their response makes me think there is something fundamental that I might be missing.

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• MySQL won't use index for query?

  - by Jack Sleight

  I have this table: CREATE TABLE `point` ( `id` INT(11) NOT NULL AUTO_INCREMENT, `siteid` INT(11) NOT NULL, `lft` INT(11) DEFAULT NULL, `rgt` INT(11) DEFAULT NULL, `level` SMALLINT(6) DEFAULT NULL, PRIMARY KEY (`id`), KEY `point_siteid_site_id` (`siteid`), CONSTRAINT `point_siteid_site_id` FOREIGN KEY (`siteid`) REFERENCES `site` (`id`) ON DELETE CASCADE ) ENGINE=INNODB AUTO_INCREMENT=35 DEFAULT CHARSET=utf8 COLLATE=utf8_unicode_ci And this query: SELECT * FROM `point` WHERE siteid = 1; Which results in this EXPLAIN information: +----+-------------+-------+------+----------------------+------+---------+------+------+-------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-------+------+----------------------+------+---------+------+------+-------------+ | 1 | SIMPLE | point | ALL | point_siteid_site_id | NULL | NULL | NULL | 6 | Using where | +----+-------------+-------+------+----------------------+------+---------+------+------+-------------+ Question is, why isn't the query using the point_siteid_site_id index?

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• C# question to ex-C++/ASM guys. Speed optimisation

  - by maxima120

  Which in C# compiled for x64 is faster? a. int * int; b. if(int < 0) int = -int; c. Math.Abs(int) I need pure theoretical answer. Please do not tell me to write loop and measure it with stopwatch. Thanks

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• How to index a date column with null values?

  - by Heinz Z.

  How should I index a date column when some rows has null values? We have to select rows between a date range and rows with null dates. We use Oracle 9.2 and higher. Options I found Using a bitmap index on the date column Using an index on date column and an index on a state field which value is 1 when the date is null Using an index on date column and an other granted not null column My thoughts to the options are: to 1: the table have to many different values to use an bitmap index to 2: I have to add an field only for this purpose and to change the query when I want to retrieve the null date rows to 3: locks tricky to add an field to an index which is not really needed What is the best practice for this case? Thanks in advance Some infos I have read: Oracle Date Index When does Oracle index null column values?

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• Oracle Sql Query taking a day long to return results using dblink

  - by Suresh S

  Guys i have the following oracle sql query that gives me the monthwise report between the dates. Basically for nov month i want sum of values between the dates 01nov to 30 nov. The table tha is being queried is residing in another database and accesssed using dblink. The DT columns is of NUMBER type (for ex 20101201) .The execution of the query is taking a day long and not completed. kindly suggest me , if their is any optimisation that can be suggested to my DBA on the dblink, or any tuning that can be done on the query , or rewriting the same. SELECT /*+ PARALLEL (A 8) */ TO_CHAR(TRUNC(TRUNC(SYSDATE,'MM')- 1,'MM'),'MONYYYY') "MONTH", TYPE AS "TYPE", COLUMN, COUNT (DISTINCT A) AS "A_COUNT", COUNT (COLUMN) AS NO_OF_COLS, SUM (DURATION) AS "SUM_DURATION", SUM (COST) AS "COST" FROM **A@LN_PROD A** WHERE DT >=TO_NUMBER(TO_CHAR(TRUNC(TRUNC(SYSDATE,'MM')-1,'MM'),'YYYYMMDD')) AND DT < TO_NUMBER(TO_CHAR(TRUNC(TRUNC(SYSDATE,'MM'),'MM'),'YYYYMMDD')) GROUP BY TYPE, COLUMN

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• Splitting tables by field to optimize MySQL?

  - by AK

  Do splitting fields into multiple tables ever yield faster queries? Consider the following two scenarios: Table1 ----------- int PersonID text Value1 float Value2 or Table1 ----------- int PersonID text Value1 Table2 ----------- int PersonID float Value2 If Value1 and Value2 are always being displayed together, I imagine Table1 is always faster because the second schema would require two SELECT statements. But are there any situations where you would choose the second? If the number of records were expected to be really large?

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