Performance and Evaluation of Lisp Systems - Dreamsongs

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§ 3.11 Data-Driven Derivative 179 Raw Time Dderiv Implementation CPU GC Real Paging 780 CL 19.00 21.60 785 CL 12.40 13.90 750 CL 32.90 45.80 730 CL Perq 72.52 750 Franz TrlOn & LclfYes TrlOn & LclfNo TrlOff & LclfYes TrlOff & LclfNo 780 Franz TrlOn & LclfYes 9.79 17.58 TrlOn & LclfNo TrlOff & LclfYes 13.52 17.28 TrlOff & LclfNo Franz 68000 TrlOn & LclfYes 16.95 92.50 TrlOn & LclfNo 20.53 96.27 TrlOff & LclfYes 21.62 92.80 TrlOff & LclfNo 50.88 96.48 Inter<strong>Lisp</strong>-10 28.06 9.12 LM-2 23.67
180 I find it rather startling that increasing memory size by 33% adds 300% in speed. — Peter Friedl<strong>and</strong> discussing a performance experiment. (November 10, 1982.)
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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 (
Page 137 and 138: 128 (defun test () (prog (ans term)
Page 139 and 140: 130 the program uses the axioms sto
Page 141 and 142: 132 Meter for Add-Lemma during TEST
Page 143 and 144: 134 Raw Time Boyer Implementation C
Page 145 and 146: 136 3.7 Browse 3.7.1 The Program ;;
Page 147 and 148: 138 ((eq (char1 (car pat)) #\*) (le
Page 149 and 150: 140 The symbol names are GENSYMed;
Page 151 and 152: 142 Meter for Match Car’s 1319800
Page 153 and 154: 144 Raw Time Browse Implementation
Page 155 and 156: 146 3.8 Destructive 3.8.1 The Progr
Page 157 and 158: 148 3.8.3 Translation Notes Meter f
Page 159 and 160: 150 3.8.4 Raw Data Raw Time Destruc
Page 161 and 162: 152 Very few programs that I have b
Page 163 and 164: 154 (defun traverse-remove (n q) (c
Page 165 and 166: 156 (defmacro init-traverse() (prog
Page 167 and 168: 158 node. The number of nodes visit
Page 169 and 170: 160 3.9.3 Translation Notes In INTE
Page 171 and 172: 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: 178 3.11.4 Raw Data Raw Time Dderiv
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 and 224: 214 3.15.4 Raw Data Raw Time Puzzle
Page 225 and 226: 216 Presumably this is because 2-di
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
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230 Raw Time Fprint Implementation
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232 3.18 File Read 3.18.1 The Progr
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234 Raw Time Fread Implementation C
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236 3.19 Terminal Print 3.19.1 The
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238 Raw Time Tprint Implementation
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240 3.20 Polynomial Manipulation 3.
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242 (defun pplus (x y) (cond ((pcoe
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244 (defun ptimes3 (y) (prog (e u c
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246 The variables in the polynomial
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248 Meter for (Bench 2) Signp’s 3
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250 3.20.3 Raw Data Raw Time Frpoly
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252 Raw Time Frpoly Power = 2 r2 =
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256 Raw Time Frpoly Power = 5 r = x
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262 Raw Time Frpoly Power = 10 r =
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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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