On Thu, 2003-09-25 at 08:15, Colin J. Williams wrote:
Nadav Horesh wrote: [snip]
As I recall, one of the targets of the numarray project is to be included as a standard python library --- shouldn't it eventually comply with the new-classes standard?
Can someone please explain what the "new-classes standard" is? (Obviously meeting it is not one of our current goals)
I hope that more consideration will be given to this as the numarray package evolves.
Of course. We don't need sub-classing internally, however, so it's difficult to predict when we'll do better.
From time to time, I am developing a matrix sub-class, it's a bit more convoluted than I would like it to be.
Posting it here would be good. Sending things directly to me tends to dumb things down... to me.
It would also be helpful if a current assessment of the speed of numarray could be published.
You asked... and... (sorry if this is less than succinct) The numarray source distribution has a module called bench.py in the Examples directory. Without any analysis, I've attached the output from bench.py on my 1.7 GHz Pentium IV running under i386 linux. Each row shows the timing numbers for numarray and Numeric for a particular function call and parameter set; the important part is the ratio ( < 1 means numarray is faster. In general, numarray is slower; this is due to the fact that numarray supports in-place operation on memory mapped non-native data formats). Suspect numbers are... suspect. Please investigate! Caveats: 1) The benchmarks were run on a multi-tasking kernel with no specific load. The benchmarks also vary from platform to platform, processor to processor, compiler to compiler, etc. These are gcc version 3.2 20020903 (Red Hat Linux 8.0 3.2-7) on a 1.7 GHz P-IV with 384M of RAM. 2) The absolute measured units (microseconds) haven't really been validated and shouldn't be taken too seriously. The relative time comparisons should be valid. 3) a,c,d are 1D arrays. b is a 2D array. 4) the 10**x column shows the logarithmic array size. 2 --> 100 elements, 6 --> 1 million elements 5) The version of bench.py which made these measurements is in CVS, not the numarray-0.7 tar-ball. Wish-list: 1) The right way to eliminate the effects of host load on the benchmarks. 2) A way to create a regression test which includes performance.
I gather that some enhancements arrived with version 0.7.
I don't recall any performance enhancements. numarray-0.7 was mostly about object array support and bug fixes. bench.py results from numarray-0.7 09-25-03: benchmark 10**x numarray (usec) Numeric (usec) numarray:Numeric take(a,c) 2 1477 25 56.8 3 1592 108 14.7 4 2920 989 2.9 5 15568 9814 1.6 6 136960 96253 1.4 put(a,c,d) 2 1673 64 26.1 3 1798 195 9.2 4 3270 1587 2.1 5 16742 15334 1.1 6 147768 151932 1.0 transpose(b) 2 154 22 6.8 3 157 24 6.3 4 190 39 4.8 5 262 74 3.5 6 257 67 3.8 putmask(a,c%2,d) 2 1415 139 10.1 3 1504 271 5.6 4 2439 1735 1.4 5 11036 17337 0.6 6 93927 157848 0.6 repeat(a,2) 2 1533 138 11.1 3 10165 579 17.5 4 103064 4701 21.9 5 1028850 58812 17.5 6 10010859 493157 20.3 choose(a%2,(a,c)) 2 1312 73 18.0 3 1389 190 7.3 4 2479 1500 1.7 5 12516 16219 0.8 6 108841 142280 0.8 ravel(b) 2 164 38 4.3 3 169 41 4.1 4 196 58 3.4 5 279 93 3.0 6 275 90 3.0 nonzero(a) 2 1044 143 7.3 3 1133 285 4.0 4 2108 1929 1.1 5 12002 17974 0.7 6 111598 176598 0.6 where(a%2,a,c) 2 1273 111 11.4 3 1380 242 5.7 4 2587 1752 1.5 5 13368 16785 0.8 6 117731 164187 0.7 compress(a%2,a) 2 2489 198 12.6 3 2661 375 7.1 4 4575 2426 1.9 5 22933 22621 1.0 6 200138 219215 0.9 diagonal(b) 2 2420 16883 0.1 3 2425 16896 0.1 4 2591 16932 0.2 5 2588 16875 0.2 6 2579 16861 0.2 trace(b) 2 2611 16988 0.2 3 2625 16990 0.2 4 2750 17004 0.2 5 2758 17048 0.2 6 2766 16942 0.2 searchsorted(a,a) 2 506 55 9.2 3 708 490 1.4 4 3054 6507 0.5 5 32101 77762 0.4 6 378341 940912 0.4 sort(a) 2 554 85 6.4 3 940 365 2.6 4 5394 3533 1.5 5 55238 36049 1.5 6 607845 420334 1.4 argsort(a) 2 1541 90 16.9 3 2061 467 4.4 4 8113 5130 1.6 5 76924 49497 1.6 6 853114 588399 1.4 argmax(b) 2 1112081 8692 127.9 3 1110896 9320 119.2 4 1108330 8706 127.3 5 1113449 8720 127.7 6 1112556 8743 127.3 array(s,typecode='i') 2 229 146 1.6 3 274 1103 0.2 4 764 10900 0.1 5 5450 105774 0.1 6 51500 1253819 0.0 dot(b,b) 2 8825828 31402493 0.3 3 8804230 32136602 0.3 4 8748239 31123564 0.3 5 8811900 32910953 0.3 6 8827314 31784649 0.3 clip(a,50,100) 2 2920 177 16.4 3 3089 370 8.3 4 5138 2471 2.1 5 24340 24737 1.0 6 211277 245540 0.9 indices((100,100)) 2 5253 2696 1.9 3 5230 2634 2.0 4 5290 2687 2.0 5 5383 2732 2.0 6 5558 2733 2.0 swapaxes(b,0,-1) 2 77 79 1.0 3 77 82 1.0 4 94 106 0.9 5 154 142 1.1 6 146 144 1.0 concatenate((a,a,a)) 2 1021 35 29.1 3 1082 48 22.1 4 1868 431 4.3 5 8843 4945 1.8 6 75181 47495 1.6 innerproduct(b,b) 2 8719124 8989714 1.0 3 8662431 8840564 1.0 4 8734306 9033604 1.0 5 8694994 8935343 1.0 6 8680599 8869750 1.0 outerproduct(b[0],b[0]) 2 42320 27598 1.5 3 42299 27802 1.5 4 42842 27935 1.5 5 42346 27822 1.5 6 42257 27709 1.5 resize(a,(2*10**i,)) 2 1708 160 10.6 3 1769 171 10.3 4 2648 468 5.6 5 9778 3515 2.8 6 75513 31778 2.4 a[0] 2 10 10 1.0 3 10 10 1.0 4 13 12 1.1 5 25 20 1.3 6 25 22 1.1 b[0] 2 46 15 3.1 3 48 17 2.8 4 56 18 3.0 5 106 33 3.1 6 100 35 2.9 a[1000:2000] 2 303 16 18.0 3 298 15 18.7 4 275 20 13.8 5 354 33 10.4 6 344 34 9.8 b[400:600,400:600] 2 320 25 12.8 3 317 25 12.2 4 348 30 11.3 5 432 46 9.2 6 418 47 8.7 arange(10.**i) 2 411 37 11.1 3 448 74 6.0 4 923 570 1.6 5 5630 5592 1.0 6 50735 54222 0.9 identity(i+2) 2 3147 226 13.9 3 3158 227 13.9 4 3214 258 12.4 5 3311 294 11.3 6 3316 291 11.4 add(a,c) 2 76 27 2.7 3 90 40 2.2 4 313 225 1.4 5 2902 2128 1.4 6 28106 19972 1.4 add(a,c,d) 2 40 22 1.9 3 46 33 1.4 4 144 132 1.1 5 1128 1165 1.0 6 10316 10827 1.0 a+c 2 91 31 2.9 3 105 45 2.4 4 335 231 1.5 5 2945 2130 1.4 6 28102 19744 1.4 cos(a) 2 84 53 1.6 3 212 235 0.9 4 1546 2351 0.7 5 15237 23074 0.7 6 151604 227898 0.7 add.reduce(a) 2 97 31 3.0 3 109 41 2.7 4 207 123 1.7 5 1062 949 1.1 6 8974 8844 1.0 add.reduce(b,0) 2 32527 9218 3.5 3 31652 9238 3.4 4 31376 9171 3.4 5 31412 9271 3.4 6 30443 9263 3.3 add.reduce(b,-1) 2 9065 9389 1.0 3 9075 9360 1.0 4 9102 9271 1.0 5 9168 9389 1.0 6 9148 9394 1.0 add.accumulate(a) 2 100 28 3.5 3 120 41 2.9 4 416 254 1.6 5 3584 2696 1.3 6 34837 25608 1.4 add.accumulate(b,0) 2 63947 25673 2.5 3 65875 25691 2.6 4 65540 25708 2.5 5 65161 25776 2.5 6 66539 25646 2.6 add.accumulate(b,1) 2 35048 25756 1.4 3 35022 25738 1.4 4 35048 25688 1.4 5 35092 25761 1.4 6 35110 25686 1.4