Performance and Evaluation of Lisp Systems - Dreamsongs

More documents

Recommendations

Info

§ 3.15 Puzzle 211 (TRIAL (LAMBDA (J) (bind (K 0) for I from 0 to TYPEMAX do (if (AND (NEQ 0 (PIECECOUNT (CLASS I))) (FIT I J)) then (SETQ K (PLACE I J)) (if (OR (TRIAL K) (ZEROP K)) then (AND PUZZLETRACEFLG (printout NIL T "Piece" .TAB "at" .TAB (ADD1 K))) (add KOUNT 1) (RETURN T) else (REMOVE! I J))) finally (PROGN (add KOUNT 1) NIL)))) (DEFINEPIECE (LAMBDA (ICLASS II JJ KK) (PROG ((INDEX 0)) (for I from 0 to II do (for J from 0 to JJ do (for K from 0 to KK do (SETQ INDEX (IPLUS I (ITIMES D (IPLUS J (ITIMES D K))))) (PASET T P III INDEX)))) (16ASET ICLASS CLASS III) (16ASET INDEX PIECEMAX III) (if (NEQ III TYPEMAX) then (add III 1)))))
212 (START (LAMBDA NIL (for M from 0 to SIZE do (PASET T PUZZLE M)) (for I from 1 to 5 do (for J from 1 to 5 do (for K from 1 to 5 do (PASET NIL PUZZLE (IPLUS I (ITIMES D (IPLUS J (ITIMES D K))))))) ) (for I from 0 to TYPEMAX do (for M from 0 to SIZE do (PASET NIL P I M))) (SETQ III 0) (DEFINEPIECE 0 3 1 0) (DEFINEPIECE 0 1 0 3) (DEFINEPIECE 0 0 3 1) (DEFINEPIECE 0 1 3 0) (DEFINEPIECE 0 3 0 1) (DEFINEPIECE 0 0 1 3) (DEFINEPIECE 1 2 0 0) (DEFINEPIECE 1 0 2 0) (DEFINEPIECE 1 0 0 2) (DEFINEPIECE 2 1 1 0) (DEFINEPIECE 2 1 0 1) (DEFINEPIECE 2 0 1 1) (DEFINEPIECE 3 1 1 1) (16ASET 13 PIECECOUNT 0) (16ASET 3 PIECECOUNT 1) (16ASET 1 PIECECOUNT 2) (16ASET 1 PIECECOUNT 3) (PROG ((M (IPLUS 1 (ITIMES D (IPLUS 1 D)))) (N 0) (KOUNT 0)) (if (FIT 0 M) then (SETQ N (PLACE 0 M)) else (printout NIL T "Error")) (if (TRIAL N) then (printout NIL T "Success in " KOUNT " trials.") else (printout NIL T "Failure.")) (TERPRI))))
Page 1 and 2:
Performance and Evaluation of Lisp
Page 3 and 4:
ii When I began the adventure on wh
Page 5 and 6:
Acknowledgements This book is reall
Page 7 and 8:
Contents Chapter 1 Introduction . .
Page 9 and 10:
Performance and Evaluation of Lisp
Page 11 and 12:
2 to be a plethora of facts, only t
Page 13 and 14:
4 An instruction pipeline is used t
Page 15 and 16:
6 to assign any location the name o
Page 17 and 18:
8 voking a closure) can be accompli
Page 19 and 20:
10 (Lisp machine, SEUS [Weyhrauch 1
Page 21 and 22:
12 which takes a function with some
Page 23 and 24:
14 free objects or until a threshol
Page 25 and 26:
16 1.1.2.6 Arithmetic Arithmetic is
Page 27 and 28:
18 Different rounding modes in the
Page 29 and 30:
20 1.3 Major Lisp Facilities There
Page 31 and 32:
22 by studying the size of the tabl
Page 33 and 34:
24 1.4.1 Know What is Being Measure
Page 35 and 36:
26 With 100 such functions and no l
Page 37 and 38:
28 the MacLisp compiler generates f
Page 39 and 40:
30 machine will perform background
Page 41 and 42:
32 2.1.2 Function Call MacLisp func
Page 43 and 44:
34 2.2 MIT CADR Symbolics Inc. and
Page 45 and 46:
36 2.3 Symbolics 2.3.1 3600 The 360
Page 47 and 48:
38 Most of the 3600’s architectur
Page 49 and 50:
40 Like the CADR, the compiler for
Page 51 and 52:
42 2.4 LMI Lambda LISP Machine, Inc
Page 53 and 54:
44 bits in the tag field of every o
Page 55 and 56:
46 2.5 S-1 Lisp S-1 Lisp runs on th
Page 57 and 58:
48 7 Program Counter 8 GC Pointer (
Page 59 and 60:
50 2.5.4 Systemic Quantities Vector
Page 61 and 62:
52 The next time FOO is invoked, th
Page 63 and 64:
54 2.7 NIL NIL (New Implementation
Page 65 and 66:
56 The arguments to the function ar
Page 67 and 68:
58 2.8 Spice Lisp Spice Lisp is an
Page 69 and 70:
60 3. Activating the frame by makin
Page 71 and 72:
62 The benchmarks were run on a Per
Page 73 and 74:
64 than there is for one without, a
Page 75 and 76:
66 2.10 Portable Standard Lisp Port
Page 77 and 78:
68 7. Finishing up and tuning 2.10.
Page 79 and 80:
70 Apollo Dn300 This is an MC68010-
Page 81 and 82:
72 2.10.5 Function Call Compiled-to
Page 83 and 84:
74 array utility package. Additiona
Page 85 and 86:
76 2.12 Data General Common Lisp Da
Page 87 and 88:
78 To determine the type, the high
Page 89 and 90:
80 2. The benchmark results contain
Page 91 and 92:
82 little else but function calls (
Page 93 and 94:
84 ;PUSH, ADJSP both do bounds ;che
Page 95 and 96:
86 Raw Time Tak Implementation CPU
Page 97 and 98:
88 Tak Times On Symbolics LM-2 4.44
Page 99 and 100:
90 Tak Times On SAIL (KLB) in MacLi
Page 101 and 102:
92 In TAKF, changing the variable F
Page 103 and 104:
94 In Vax NIL, there appears to be
Page 105 and 106:
96 3.2.4 Raw Data Raw Time Stak Imp
Page 107 and 108:
98 As Peter Deutsch has pointed out
Page 109 and 110:
100 that THROW uses is linear in th
Page 111 and 112:
102 3.3.4 Raw Data Raw Time Ctak Im
Page 113 and 114:
104 It measures EBOX milliseconds.
Page 115 and 116:
106 Because of the average size of
Page 117 and 118:
108 Raw Time Takl Implementation CP
Page 119 and 120:
110 3.5 Takr 3.5.1 The Program (def
Page 121 and 122:
112 of 8.2. Meter for Tak0 TAK0 817
Page 123 and 124:
114 Raw Time Takr Implementation CP
Page 125 and 126:
116 3.6 Boyer Here is what Bob Boye
Page 127 and 128:
118 (defun rewrite-with-lemmas (ter
Page 129 and 130:
120 (equal (equal (plus a b) (zero)
Page 131 and 132:
122 (equal (power-eval (big-plus x
Page 133 and 134:
124 (equal (numberp (greatest-facto
Page 135 and 136:
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 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: 210 (CONSTANTS SIZE TYPEMAX D CLASS
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
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 273 and 274:
264 Raw Time Frpoly Power = 10 r2 =
Page 275 and 276:
Page 277 and 278:
268 Raw Time Frpoly Power = 15 r =
Page 279 and 280:
Page 281 and 282:
Page 283 and 284:
274 Well, it certainly is hard to e
Page 285 and 286:
276 sophisticated Lisp implementati
Page 287 and 288:
278 [Foderaro 1982] Foderaro, J. K.
Page 289 and 290:
280
Page 291 and 292:
282 context-switching, 7. Cray-1, 4
Page 293 and 294:
284 multi-processing Lisp, 7. MV400
show all

Performance and Evaluation of Lisp Systems - Dreamsongs

Create successful ePaper yourself

Delete template?

Save as template?