Performance and Evaluation of Lisp Systems - Dreamsongs

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§ 3.15 Puzzle 215 Raw Time Puzzle Implementation CPU GC Real Paging 780 CL 47.48 0.00 785 CL 29.60 0.00 750 CL 231.79 0.00 730 CL 512.56 0.00 Perq 75.80 750 Franz TrlOn & LclfYes TrlOn & LclfNo TrlOff & LclfYes TrlOff & LclfNo 780 Franz TrlOn & LclfYes TrlOn & LclfNo TrlOff & LclfYes TrlOff & LclfNo Franz 68000 TrlOn & LclfYes 52.12 32.60 TrlOn & LclfNo 51.32 31.67 TrlOff & LclfYes 52.08 25.18 TrlOff & LclfNo 52.80 24.45 Inter<strong>Lisp</strong>-10 121.02 7.92 LM-2 42.93
216 Presumably this is because 2-dimensional array reference is losing its lunch. — Glenn Burke explaining why PUZZLE runs slowly. (August 5, 1983.)
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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 (
Page 173 and 174: 164 (TIMIT (LAMBDA NIL (TIMEALL (SE
Page 175 and 176: 166 Raw Time Traverse Initializatio
Page 177 and 178: 168 Raw Time Traverse Implementatio
Page 179 and 180: 170 3.10 Derivative 3.10.1 The Prog
Page 181 and 182: 172 3.10.4 Raw Data Raw Time Deriv
Page 183 and 184: 174 The benchmark should solve a pr
Page 185 and 186: 176 (defun dderiv (a) (cond ((atom
Page 187 and 188: 178 3.11.4 Raw Data Raw Time Dderiv
Page 189 and 190: 180 I find it rather startling that
Page 191 and 192: 182 3.12.2 Analysis This benchmark
Page 193 and 194: 184 Raw Time Fdderiv Implementation
Page 195 and 196: 186 3.13 Division by 2 3.13.1 The P
Page 197 and 198: 188 3.13.4 Raw Data Raw Time Iterat
Page 199 and 200: 190 Raw Time Recursive Div2 Impleme
Page 201 and 202: 192 If naive users write cruddy cod
Page 203 and 204: 194 l6 (cond ((< k j) (setq j (- j
Page 205 and 206: 196 3.14.3 Translation Notes The IN
Page 207 and 208: 198 (for J to LE1 do (* Loop thru b
Page 209 and 210: 200 3.14.4 Raw Data Raw Time FFT Im
Page 211 and 212: 202 This is not to say Franz’s co
Page 213 and 214: 204 (defun place (i j) (let ((end (
Page 215 and 216: 206 (definePiece 2 1 0 1) (definePi
Page 217 and 218: 208 During the search, the first ei
Page 219 and 220: 210 (CONSTANTS SIZE TYPEMAX D CLASS
Page 221 and 222: 212 (START (LAMBDA NIL (for M from
Page 223: 214 3.15.4 Raw Data Raw Time Puzzle
Page 227 and 228: 218 (defun try (i depth) (cond ((=
Page 229 and 230: 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 =
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266 Raw Time Frpoly Power = 10 r3 =
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268 Raw Time Frpoly Power = 15 r =
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274 Well, it certainly is hard to e
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276 sophisticated Lisp implementati
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278 [Foderaro 1982] Foderaro, J. K.
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280
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282 context-switching, 7. Cray-1, 4
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284 multi-processing Lisp, 7. MV400
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