Tsukuba.R#9 appendix2

plots.R

library(ggplot2)

# inline でのプロット 省略の波は Keynote のテキストをはっつけた
inline <- data.frame(x=c("C", "RCpp", "Pure R"), y=c(0.219, 0.242, 27.969))
ggplot(inline, aes(x=x, y=y))
+ geom_bar(aes(fill=x))           # x ごとに色づけ
+ coord_cartesian(ylim=c(0, 0.5)) # y 軸を制限 (はみでる barplot は scale_y_continuous で制限はできない)
+ scale_y_continuous(name="elapsed",
                     breaks=c(0, 0.1, 0.2, 0.3, 0.4, 0.5), # 適当に軸を分割する
                     labels=c(0, 0.1, 0.2, 26, 27, 28))    # フリーダムにラベルを張る
+ theme_grey(base_size=16)        # ちょっとフォントサイズを大きくする
ggsave("inline.pdf")              # 保存する

# inline でのコード行数比較
codeline <- data.frame(x=c("inline", "outside"), y=c(16, 22))
ggplot(codeline, aes(x=x, y=y))
+ geom_bar()
+ scale_y_continuous(name="line of code")
+ coord_cartesian(ylim=c(10, 22))
ggsave("codeline.pdf")

# compiler でのプロット
compiler <- data.frame(x=c("C", "RCpp", "Pure R", "Compiler"),
                       y=c(0.219, 0.242, 27.969, 10.604))
ggplot(compiler, aes(x=x, y=y))
+ geom_bar(aes(fill=x))
+ scale_y_continuous(name="elapsed", breaks=1000*(1:9))
+ theme_grey(base_size=16)
+ coord_cartesian(ylim=c(0, 10000))
ggsave("compiler.pdf")

# revolution での プロット
# legend が重なるように Keynote で重ねたのであって、ggplot2 の facet を使っているわけではない
desc <- "
ベンチマーク結果を以下のように整形する 僕は Vim で頑張った
ファイルはこの gist に張ってある
<== Test period ==> : <== Method name (trimmed "-* (sec)") ==> : <== score time ==>
"
benches <- lapply(c("r-bare.txt", "revo1core.txt", "revo2core.txt"),
                  function (file) read.csv(file, sep=":", header=F))

ggplot(benches[[1]], aes(x=V1, y=V3))                    # bare r の結果が入ってる
+ geom_bar(aes(fill=V2))                                 # テストケースごとに色わけ
+ scale_x_discrete(name=""
                   labels=c("I", "II", "III", "TOTAL"),  # ラベルを振る
                   breaks=levels(benches[[1]]$V1))       # discreate なので breaks は levels
+ scale_y_continuous(name="sec")
+ theme_grey(base_size=12)
ggsave("bare-R.pdf")

ggplot(benches[[2]], aes(x=V1, y=V3))                    # revolution 1-core の結果が入ってる
+ geom_bar(aes(fill=V2))
+ scale_x_discrete(name=""
                   labels=c("I", "II", "III", "TOTAL"), 
                   breaks=levels(benches[[2]]$V1))
+ scale_y_continuous(name="sec")
+ theme_grey(base_size=12)
ggsave("revo1core.pdf")

ggplot(benches[[3]], aes(x=V1, y=V3))                    # revolution 2-core の結果が入ってる
+ geom_bar(aes(fill=V2))
+ scale_x_discrete(name=""
                   labels=c("I", "II", "III", "TOTAL"), 
                   breaks=levels(benches[[3]]$V1))
+ scale_y_continuous(name="sec")
+ theme_grey(base_size=12)
ggsave("revo2core.pdf")

r-bare.txt

I. Matrix calculation:Creation, transp., deformation of a 2500x2500 matrix:  1.49133333333333 
I. Matrix calculation:2400x2400 normal distributed random matrix ^1000:  0.720333333333333 
I. Matrix calculation:Sorting of 7,000,000 random values:  1.13333333333333 
I. Matrix calculation:2800x2800 cross-product matrix (b = a' * a):  19.3946666666667 
I. Matrix calculation:Linear regr. over a 3000x3000 matrix (c = a \ b'):  8.623 
II. Matrix functions:FFT over 2,400,000 random values:  0.816333333333333 
II. Matrix functions:Eigenvalues of a 640x640 random matrix:  1.31733333333333 
II. Matrix functions:Determinant of a 2500x2500 random matrix:  5.24366666666667 
II. Matrix functions:Cholesky decomposition of a 3000x3000 matrix:  6.48833333333333 
II. Matrix functions:Inverse of a 1600x1600 random matrix:  5.32899999999999 
III. Programmation:3,500,000 Fibonacci numbers calculation (vector calc)(sec):  1.17899999999999 
III. Programmation:Creation of a 3000x3000 Hilbert matrix (matrix calc):  0.735666666666665 
III. Programmation:Grand common divisors of 400,000 pairs (recursion):  3.47299999999998 
III. Programmation:Creation of a 500x500 Toeplitz matrix (loops):  1.34400000000001 
III. Programmation:Escoufier's method on a 45x45 matrix (mixed):  0.951999999999998 
TOTAL:Total time for all 15 tests:  58.241 
TOTAL:Overall mean (sum of I, II and III trimmed means/3):  2.10437988311317 

revo1core.txt

I. Matrix calculation:Creation, transp., deformation of a 2500x2500 matrix:  4.56066666666667 
I. Matrix calculation:2400x2400 normal distributed random matrix ^1000:  0.707333333333333 
I. Matrix calculation:Sorting of 7,000,000 random values:  1.09066666666667 
I. Matrix calculation:2800x2800 cross-product matrix (b = a' * a):  3.93266666666667 
I. Matrix calculation:Linear regr. over a 3000x3000 matrix (c = a \ b'):  1.97433333333333 
II. Matrix functions:FFT over 2,400,000 random values:  0.791333333333332 
II. Matrix functions:Eigenvalues of a 640x640 random matrix:  0.786666666666667 
II. Matrix functions:Determinant of a 2500x2500 random matrix:  1.78866666666666 
II. Matrix functions:Cholesky decomposition of a 3000x3000 matrix:  1.69266666666666 
II. Matrix functions:Inverse of a 1600x1600 random matrix:  1.50366666666667 
III. Programmation:3,500,000 Fibonacci numbers calculation (vector calc)(sec):  1.13533333333333 
III. Programmation:Creation of a 3000x3000 Hilbert matrix (matrix calc):  0.743333333333335 
III. Programmation:Grand common divisors of 400,000 pairs (recursion):  4.21633333333333 
III. Programmation:Creation of a 500x500 Toeplitz matrix (loops):  1.23666666666668 
III. Programmation:Escoufier's method on a 45x45 matrix (mixed):  0.717999999999989 
TOTAL:Total time for all 15 tests:  26.8783333333333 
TOTAL:Overall mean (sum of I, II and III trimmed means/3):  1.37701279516068 

revo2core.txt

I. Matrix calculation:Creation, transp., deformation of a 2500x2500 matrix:  1.89 
I. Matrix calculation:2400x2400 normal distributed random matrix ^1000:  0.750333333333333 
I. Matrix calculation:Sorting of 7,000,000 random values:  1.087 
I. Matrix calculation:2800x2800 cross-product matrix (b = a' * a):  2.55766666666667 
I. Matrix calculation:Linear regr. over a 3000x3000 matrix (c = a \ b'):  1.28533333333333 
II. Matrix functions:FFT over 2,400,000 random values:  0.851999999999999 
II. Matrix functions:Eigenvalues of a 640x640 random matrix:  0.859 
II. Matrix functions:Determinant of a 2500x2500 random matrix:  1.13533333333333 
II. Matrix functions:Cholesky decomposition of a 3000x3000 matrix:  1.03333333333333 
II. Matrix functions:Inverse of a 1600x1600 random matrix:  1.20533333333333 
III. Programmation:3,500,000 Fibonacci numbers calculation (vector calc)(sec):  1.12033333333333 
III. Programmation:Creation of a 3000x3000 Hilbert matrix (matrix calc):  0.736000000000004 
III. Programmation:Grand common divisors of 400,000 pairs (recursion):  4.667 
III. Programmation:Creation of a 500x500 Toeplitz matrix (loops):  1.34633333333333 
III. Programmation:Escoufier's method on a 45x45 matrix (mixed):  0.982 
TOTAL:Total time for all 15 tests:  21.507 
TOTAL:Overall mean (sum of I, II and III trimmed means/3):  1.16462514246633