Performance and Evaluation of Lisp Systems - Dreamsongs

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§ 3.12 Another Data-Driven Derivative 185 The point is that we are concerned about what will happen when real users use the thing for program development, not what happens when the implementors optimize performance. — Charles Hedrick philosophizing on benchmarking. (May 30, 1983.)
186 3.13 Division by 2 3.13.1 The Program ;;; DIV2 -- Benchmark that divides by 2 using lists of n NIL’s. ;;; This file contains a recursive as well as an iterative test. (defun create-n (n) (do ((n n (1- n)) (a () (push () a))) ((= n 0) a))) (defvar ll (create-n 200.)) (defun iterative-div2 (l) (do ((l l (cddr l)) (a () (push (car l) a))) ((null l) a))) (defun recursive-div2 (l) (cond ((null l) ()) (t (cons (car l) (recursive-div2 (cddr l)))))) (defun test-1 (l) (do ((i 300. (1- i))) ((= i 0)) (iterative-div2 l) (iterative-div2 l) (iterative-div2 l) (iterative-div2 l))) (defun test-2 (l) (do ((i 300. (1- i))) ((= i 0)) (recursive-div2 l) (recursive-div2 l) (recursive-div2 l) (recursive-div2 l))) ;;; for the iterative test call: (test-1 ll) ;;; for the recursive test call: (test-2 ll) 3.13.2 Analysis In this benchmark, numbers are represented in unary form: n is represented as a list of n ()’s. The benchmark divides numbers in this representation by 2 by returning a list of every other () in the original list. There are two versions of this program: One is the simple recursive definition, and the other is an iterative definition. The test is to divide 200 by 2 1200 times for each version.
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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
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 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: 184 Raw Time Fdderiv Implementation
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
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
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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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