Performance and Evaluation of Lisp Systems - Dreamsongs

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§ 3.20 Polynomial Manipulation 273 Raw Time Frpoly Power = 15 r3 = r in flonums Implementation CPU GC Real Paging 780 CL 17.83 13.78 785 CL 10.40 0.00 750 CL 31.05 26.80 730 CL 85.16 61.52 Perq 750 Franz TrlOn & LclfYes 36.69 11.20 TrlOn & LclfNo 41.47 11.00 TrlOff & LclfYes 36.69 11.20 TrlOff & LclfNo 86.83 11.22 780 Franz TrlOn & LclfYes 21.70 6.97 TrlOn & LclfNo 21.70 6.97 TrlOff & LclfYes 25.08 6.77 TrlOff & LclfNo 54.27 6.95 Franz 68000 TrlOn & LclfYes 37.60 20.53 TrlOn & LclfNo 49.18 17.18 TrlOff & LclfYes 40.88 20.55 TrlOff & LclfNo 79.72 17.23 InterLisp-10 LM-2
274 Well, it certainly is hard to explain because there are several different cases. There is a prefetcher. . . . — Bruce Edwardsexplaining the 3600’s instruction cache. (September 18, 1984.) This article couldn’t do nearly as much damage as the Gabriel and Brooks “critique” or the upcoming publication of last year’s Lisp benchmarks by MIT Press. — Anonymous commenting on the author of this book. (April 1985) Would you like to volunteer? — Peter Deutsch—the question I should have said ‘no’ to. (February 27, 1981)
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Performance and Evaluation of Lisp
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ii When I began the adventure on wh
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Acknowledgements This book is reall
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Contents Chapter 1 Introduction . .
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Performance and Evaluation of Lisp
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2 to be a plethora of facts, only t
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4 An instruction pipeline is used t
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6 to assign any location the name o
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8 voking a closure) can be accompli
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10 (Lisp machine, SEUS [Weyhrauch 1
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12 which takes a function with some
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14 free objects or until a threshol
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16 1.1.2.6 Arithmetic Arithmetic is
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18 Different rounding modes in the
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20 1.3 Major Lisp Facilities There
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22 by studying the size of the tabl
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24 1.4.1 Know What is Being Measure
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26 With 100 such functions and no l
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28 the MacLisp compiler generates f
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30 machine will perform background
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32 2.1.2 Function Call MacLisp func
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34 2.2 MIT CADR Symbolics Inc. and
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36 2.3 Symbolics 2.3.1 3600 The 360
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38 Most of the 3600’s architectur
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40 Like the CADR, the compiler for
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42 2.4 LMI Lambda LISP Machine, Inc
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44 bits in the tag field of every o
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46 2.5 S-1 Lisp S-1 Lisp runs on th
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48 7 Program Counter 8 GC Pointer (
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50 2.5.4 Systemic Quantities Vector
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52 The next time FOO is invoked, th
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54 2.7 NIL NIL (New Implementation
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56 The arguments to the function ar
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58 2.8 Spice Lisp Spice Lisp is an
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60 3. Activating the frame by makin
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62 The benchmarks were run on a Per
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64 than there is for one without, a
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66 2.10 Portable Standard Lisp Port
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68 7. Finishing up and tuning 2.10.
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70 Apollo Dn300 This is an MC68010-
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72 2.10.5 Function Call Compiled-to
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74 array utility package. Additiona
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76 2.12 Data General Common Lisp Da
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78 To determine the type, the high
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80 2. The benchmark results contain
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82 little else but function calls (
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84 ;PUSH, ADJSP both do bounds ;che
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86 Raw Time Tak Implementation CPU
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88 Tak Times On Symbolics LM-2 4.44
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90 Tak Times On SAIL (KLB) in MacLi
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92 In TAKF, changing the variable F
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94 In Vax NIL, there appears to be
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96 3.2.4 Raw Data Raw Time Stak Imp
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98 As Peter Deutsch has pointed out
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100 that THROW uses is linear in th
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102 3.3.4 Raw Data Raw Time Ctak Im
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104 It measures EBOX milliseconds.
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106 Because of the average size of
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108 Raw Time Takl Implementation CP
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110 3.5 Takr 3.5.1 The Program (def
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112 of 8.2. Meter for Tak0 TAK0 817
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114 Raw Time Takr Implementation CP
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116 3.6 Boyer Here is what Bob Boye
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118 (defun rewrite-with-lemmas (ter
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120 (equal (equal (plus a b) (zero)
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122 (equal (power-eval (big-plus x
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124 (equal (numberp (greatest-facto
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126 (equal (times x (add1 y)) (if (
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128 (defun test () (prog (ans term)
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130 the program uses the axioms sto
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132 Meter for Add-Lemma during TEST
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134 Raw Time Boyer Implementation C
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136 3.7 Browse 3.7.1 The Program ;;
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138 ((eq (char1 (car pat)) #\*) (le
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140 The symbol names are GENSYMed;
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142 Meter for Match Car’s 1319800
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144 Raw Time Browse Implementation
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146 3.8 Destructive 3.8.1 The Progr
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148 3.8.3 Translation Notes Meter f
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150 3.8.4 Raw Data Raw Time Destruc
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152 Very few programs that I have b
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154 (defun traverse-remove (n q) (c
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156 (defmacro init-traverse() (prog
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158 node. The number of nodes visit
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160 3.9.3 Translation Notes In INTE
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162 (TSELECT (LAMBDA (N Q) (for N (
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164 (TIMIT (LAMBDA NIL (TIMEALL (SE
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166 Raw Time Traverse Initializatio
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168 Raw Time Traverse Implementatio
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170 3.10 Derivative 3.10.1 The Prog
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172 3.10.4 Raw Data Raw Time Deriv
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174 The benchmark should solve a pr
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176 (defun dderiv (a) (cond ((atom
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178 3.11.4 Raw Data Raw Time Dderiv
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180 I find it rather startling that
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182 3.12.2 Analysis This benchmark
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184 Raw Time Fdderiv Implementation
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186 3.13 Division by 2 3.13.1 The P
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188 3.13.4 Raw Data Raw Time Iterat
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190 Raw Time Recursive Div2 Impleme
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192 If naive users write cruddy cod
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194 l6 (cond ((< k j) (setq j (- j
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196 3.14.3 Translation Notes The IN
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198 (for J to LE1 do (* Loop thru b
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200 3.14.4 Raw Data Raw Time FFT Im
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202 This is not to say Franz’s co
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204 (defun place (i j) (let ((end (
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206 (definePiece 2 1 0 1) (definePi
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208 During the search, the first ei
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210 (CONSTANTS SIZE TYPEMAX D CLASS
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212 (START (LAMBDA NIL (for M from
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214 3.15.4 Raw Data Raw Time Puzzle
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216 Presumably this is because 2-di
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218 (defun try (i depth) (cond ((=
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220 Meter for Try =’s 19587224 Re
Page 231 and 232: 222 (LAST-POSITION (LAMBDA NIL (OR
Page 233 and 234: 224 3.16.4 Raw Data Raw Time Triang
Page 235 and 236: 226 I also tried TRIANG, but gave u
Page 237 and 238: 228 The test pattern is a tree that
Page 239 and 240: 230 Raw Time Fprint Implementation
Page 241 and 242: 232 3.18 File Read 3.18.1 The Progr
Page 243 and 244: 234 Raw Time Fread Implementation C
Page 245 and 246: 236 3.19 Terminal Print 3.19.1 The
Page 247 and 248: 238 Raw Time Tprint Implementation
Page 249 and 250: 240 3.20 Polynomial Manipulation 3.
Page 251 and 252: 242 (defun pplus (x y) (cond ((pcoe
Page 253 and 254: 244 (defun ptimes3 (y) (prog (e u c
Page 255 and 256: 246 The variables in the polynomial
Page 257 and 258: 248 Meter for (Bench 2) Signp’s 3
Page 259 and 260: 250 3.20.3 Raw Data Raw Time Frpoly
Page 261 and 262: 252 Raw Time Frpoly Power = 2 r2 =
Page 265 and 266: 256 Raw Time Frpoly Power = 5 r = x
Page 271 and 272: 262 Raw Time Frpoly Power = 10 r =
Page 277 and 278: 268 Raw Time Frpoly Power = 15 r =
Page 281: 272 Raw Time Frpoly Power = 15 r3 =
Page 285 and 286: 276 sophisticated Lisp implementati
Page 287 and 288: 278 [Foderaro 1982] Foderaro, J. K.
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Page 291 and 292: 282 context-switching, 7. Cray-1, 4
Page 293 and 294: 284 multi-processing Lisp, 7. MV400
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