Performance and Evaluation of Lisp Systems - Dreamsongs

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The following people ran benchmarks and provided a great deal of support and information about the benchmark results: Raymond Bates, Alan Bawden, Eric Benson, Bob Boyer, Rod Brooks, Gary Brown, Howard Cannon, George Carrette, Scott Fahlman, Mike Farmwald, William Galway, Jim Gay, Erik Gilbert, Joe Ginder, Bernie Greenberg, Richard Greenblatt, Carl W. Hoffman, Earl Killian, Paul Martin, Dave Moon, Vaughan Pratt, Tom Rindfleisch, Eric Schoen, Beau Sheil, Guy L. Steele, Jr., Bill vanMelle, and Dan Weinreb. Virginia Kean did a major editing job on this book. Palo Alto, California May 1985 v — R. P. G.
Contents Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . 1 1.1 Levels of Lisp System Architecture . . . . . . . . . . . . . . . . 2 1.2 Lisp Operation Level . . . . . . . . . . . . . . . . . . . . . . 18 1.3 Major Lisp Facilities . . . . . . . . . . . . . . . . . . . . . . . 20 1.4 The Art of Benchmarking . . . . . . . . . . . . . . . . . . . . 23 Chapter 2 The Implementations . . . . . . . . . . . . . . 31 2.1 MacLisp . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.2 MIT CADR . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.3 Symbolics . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.4 LMI Lambda . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.5 S-1 Lisp . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2.6 Franz Lisp . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 2.7 NIL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 2.8 Spice Lisp . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 2.9 Vax Common Lisp . . . . . . . . . . . . . . . . . . . . . . . 63 2.10 Portable Standard Lisp . . . . . . . . . . . . . . . . . . . . . 66 2.11 Xerox D-Machine . . . . . . . . . . . . . . . . . . . . . . . 73 Chapter 3 The Benchmarks . . . . . . . . . . . . . . . . 81 3.1 Tak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 3.2 Stak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 3.3 Ctak . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 3.4 Takl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 3.5 Takr . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 3.6 Boyer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 3.7 Browse . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 3.8 Destructive . . . . . . . . . . . . . . . . . . . . . . . . . . 146 3.9 Traverse . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 3.10 Derivative . . . . . . . . . . . . . . . . . . . . . . . . . . 170 3.11 Data-Driven Derivative . . . . . . . . . . . . . . . . . . . . 175 3.12 Another Data-Driven Derivative . . . . . . . . . . . . . . . . 181 3.13 Division by 2 . . . . . . . . . . . . . . . . . . . . . . . . 186 3.14 FFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 3.15 Puzzle . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 3.16 Triangle . . . . . . . . . . . . . . . . . . . . . . . . . . 217
Page 1 and 2: Performance and Evaluation of Lisp
Page 3 and 4: ii When I began the adventure on wh
Page 5: Acknowledgements This book is reall
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
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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
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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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Performance and Evaluation of Lisp Systems - Dreamsongs

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