Performance and Evaluation of Lisp Systems - Dreamsongs

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279 [Weinreb 1981] Weinreb, Daniel, and Moon, David. LISP Machine Manual, Fourth Edition. Massachusetts Institute of Technology Artificial Intelligence Laboratory, Cambridge, Massachusetts, July 1981. [Weyhrauch 1981] Weyhrauch, R. W., Talcott, C. T., Scherlis, W. L., Gabriel, R. P.; personal communication and involvement. [White 1979] White, J. L., NIL: A Perspective, Proceedings of the 1979 MAC- SYMA Users Conference, July 1979.
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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
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222 (LAST-POSITION (LAMBDA NIL (OR
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224 3.16.4 Raw Data Raw Time Triang
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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 283 and 284: 274 Well, it certainly is hard to e
Page 285 and 286: 276 sophisticated Lisp implementati
Page 287: 278 [Foderaro 1982] Foderaro, J. K.
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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