Add dataset attrset in garlic
Modify nbody to evenly distribute blocks per cpu
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06c29b573f
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8ce88ef046
@ -8,7 +8,7 @@
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# Leave the first CPU per socket unused?
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# Leave the first CPU per socket unused?
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, freeCpu ? false
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, freeCpu ? false
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, particles ? 1024 * 32
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, particles ? 4096 * 24
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}:
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}:
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with stdenv.lib;
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with stdenv.lib;
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@ -16,8 +16,7 @@ with stdenv.lib;
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let
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let
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# Initial variable configuration
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# Initial variable configuration
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varConf = with bsc; {
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varConf = with bsc; {
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# We need at least cpusPerNode blocks
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nblocks = [ 12 24 48 96 192 384 768 ];
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nblocks = [ 4 8 16 32 64 128 256 512 ];
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};
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};
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machineConfig = targetMachine.config;
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machineConfig = targetMachine.config;
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@ -6,29 +6,34 @@ library(jsonlite)
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args=commandArgs(trailingOnly=TRUE)
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args=commandArgs(trailingOnly=TRUE)
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# Read the timetable from args[1]
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# Read the timetable from args[1]
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input_file = "timetable.json.gz"
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input_file = "input.json"
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if (length(args)>0) { input_file = args[1] }
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if (length(args)>0) { input_file = args[1] }
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# Load the dataset in NDJSON format
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# Load the dataset in NDJSON format
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dataset = jsonlite::stream_in(file(input_file)) %>%
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dataset = jsonlite::stream_in(file(input_file)) %>%
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jsonlite::flatten()
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jsonlite::flatten()
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# We only need the cpu bind, blocksize and time
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particles = unique(dataset$config.particles)
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df = select(dataset, config.freeCpu, config.blocksize, time) %>%
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rename(blocksize=config.blocksize, freeCpu=config.freeCpu)
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# We only need the cpu bind, nblocks and time
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df = select(dataset, config.freeCpu, config.nblocks, config.hw.cpusPerSocket, time) %>%
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rename(nblocks=config.nblocks,
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freeCpu=config.freeCpu,
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cpusPerSocket=config.hw.cpusPerSocket)
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df = df %>% mutate(blocksPerCpu = nblocks / cpusPerSocket)
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# Use the blocksize as factor
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df$blocksize = as.factor(df$blocksize)
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df$freeCpu = as.factor(df$freeCpu)
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df$freeCpu = as.factor(df$freeCpu)
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df$nblocks = as.factor(df$nblocks)
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df$blocksPerCpuFactor = as.factor(df$blocksPerCpu)
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# Split by malloc variant
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# Split by malloc variant
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D=df %>% group_by(freeCpu, blocksize) %>%
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D=df %>% group_by(freeCpu, nblocks) %>%
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mutate(tnorm = time / median(time) - 1)
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mutate(tnorm = time / median(time) - 1)
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bs_unique = unique(df$blocksize)
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bs_unique = unique(df$nblocks)
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nbs=length(bs_unique)
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nbs=length(bs_unique)
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print(D)
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print(D)
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ppi=300
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ppi=300
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@ -39,12 +44,12 @@ png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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#
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#
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#
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#
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# Create the plot with the normalized time vs blocksize
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# Create the plot with the normalized time vs nblocks
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p = ggplot(data=D, aes(x=blocksize, y=tnorm)) +
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p = ggplot(data=D, aes(x=blocksPerCpuFactor, y=tnorm)) +
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# Labels
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# Labels
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labs(x="Block size", y="Normalized time",
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labs(x="Blocks/CPU", y="Normalized time",
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title="Nbody normalized time",
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title=sprintf("Nbody normalized time. Particles=%d", particles),
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subtitle=input_file) +
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subtitle=input_file) +
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# Center the title
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# Center the title
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@ -85,14 +90,15 @@ dev.off()
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#
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#
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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## Create the plot with the normalized time vs blocksize
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## Create the plot with the normalized time vs nblocks
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p = ggplot(D, aes(x=blocksize, y=time, color=freeCpu)) +
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p = ggplot(D, aes(x=blocksPerCpuFactor, y=time, color=freeCpu)) +
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labs(x="Block size", y="Time (s)",
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labs(x="Blocks/CPU", y="Time (s)",
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title="Nbody granularity",
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title=sprintf("Nbody granularity. Particles=%d", particles),
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subtitle=input_file) +
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subtitle=input_file) +
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theme_bw() +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.5, 0.88)) +
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geom_point(shape=21, size=3) +
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geom_point(shape=21, size=3) +
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#scale_x_continuous(trans=log2_trans()) +
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#scale_x_continuous(trans=log2_trans()) +
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@ -6,30 +6,36 @@ library(jsonlite)
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args=commandArgs(trailingOnly=TRUE)
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args=commandArgs(trailingOnly=TRUE)
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# Read the timetable from args[1]
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# Read the timetable from args[1]
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input_file = "timetable.json.gz"
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input_file = "input.json"
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if (length(args)>0) { input_file = args[1] }
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if (length(args)>0) { input_file = args[1] }
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# Load the dataset in NDJSON format
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# Load the dataset in NDJSON format
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dataset = jsonlite::stream_in(file(input_file)) %>%
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dataset = jsonlite::stream_in(file(input_file)) %>%
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jsonlite::flatten()
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jsonlite::flatten()
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# We only need the cpu bind, blocksize and time
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particles = unique(dataset$config.particles)
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df = select(dataset, config.enableJemalloc, config.blocksize, time) %>%
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rename(blocksize=config.blocksize,
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# We only need the cpu bind, nblocks and time
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jemalloc=config.enableJemalloc)
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df = select(dataset, config.enableJemalloc, config.nblocks, config.hw.cpusPerSocket, time) %>%
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rename(nblocks=config.nblocks,
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jemalloc=config.enableJemalloc,
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cpusPerSocket=config.hw.cpusPerSocket)
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df = df %>% mutate(blocksPerCpu = nblocks / cpusPerSocket)
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# Use the blocksize as factor
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df$blocksize = as.factor(df$blocksize)
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df$jemalloc = as.factor(df$jemalloc)
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df$jemalloc = as.factor(df$jemalloc)
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df$nblocks = as.factor(df$nblocks)
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df$blocksPerCpuFactor = as.factor(df$blocksPerCpu)
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# Split by malloc variant
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# Split by malloc variant
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D=df %>% group_by(jemalloc, blocksize) %>%
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D=df %>% group_by(jemalloc, nblocks) %>%
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mutate(tnorm = time / median(time) - 1)
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mutate(tnorm = time / median(time) - 1)
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# Add another column: blocksPerCpu (we assume one task per socket, using
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# all CPUs)
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bs_unique = unique(df$blocksize)
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bs_unique = unique(df$nblocks)
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nbs=length(bs_unique)
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nbs=length(bs_unique)
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print(D)
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print(D)
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ppi=300
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ppi=300
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@ -40,12 +46,12 @@ png("box.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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#
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#
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#
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#
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# Create the plot with the normalized time vs blocksize
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# Create the plot with the normalized time vs nblocks
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p = ggplot(data=D, aes(x=blocksize, y=tnorm)) +
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p = ggplot(data=D, aes(x=nblocks, y=tnorm)) +
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# Labels
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# Labels
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labs(x="Block size", y="Normalized time",
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labs(x="Num blocks", y="Normalized time",
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title="Nbody normalized time",
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title=sprintf("Nbody normalized time. Particles=%d", particles),
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subtitle=input_file) +
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subtitle=input_file) +
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# Center the title
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# Center the title
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@ -62,8 +68,7 @@ p = ggplot(data=D, aes(x=blocksize, y=tnorm)) +
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geom_boxplot(aes(fill=jemalloc)) +
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geom_boxplot(aes(fill=jemalloc)) +
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# # Use log2 scale in x
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# # Use log2 scale in x
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# scale_x_continuous(trans=log2_trans(),
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# scale_x_continuous(trans=log2_trans()) +
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# breaks=bs_unique) +
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#
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#
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scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
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@ -86,17 +91,18 @@ dev.off()
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#
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#
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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png("scatter.png", width=w*ppi, height=h*ppi, res=ppi)
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#
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#
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## Create the plot with the normalized time vs blocksize
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## Create the plot with the normalized time vs nblocks
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p = ggplot(D, aes(x=blocksize, y=time, color=jemalloc)) +
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p = ggplot(D, aes(x=blocksPerCpu, y=time, color=jemalloc)) +
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labs(x="Block size", y="Time (s)",
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labs(x="Blocks/CPU", y="Time (s)",
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title="Nbody granularity",
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title=sprintf("Nbody granularity. Particles=%d", particles),
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subtitle=input_file) +
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subtitle=input_file) +
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theme_bw() +
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theme_bw() +
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theme(plot.subtitle=element_text(size=8)) +
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theme(plot.subtitle=element_text(size=8)) +
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theme(legend.position = c(0.5, 0.88)) +
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geom_point(shape=21, size=3) +
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geom_point(shape=21, size=3) +
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#scale_x_continuous(trans=log2_trans()) +
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scale_x_continuous(trans=log2_trans()) +
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scale_y_continuous(trans=log2_trans())
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scale_y_continuous(trans=log2_trans())
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# Render the plot
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# Render the plot
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@ -20,8 +20,7 @@ stdenv.mkDerivation {
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conf=garlic_config.json
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conf=garlic_config.json
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for run in $(ls -d [0-9]* | sort -n); do
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for run in $(ls -d [0-9]* | sort -n); do
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time=$(awk '/^time /{print $2}' $run/stdout.log)
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time=$(awk '/^time /{print $2}' $run/stdout.log)
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jq -cn "{ exp:\"$exp\", unit:\"$unit\", config:inputs, time:$time}" \
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jq -cn "{ exp:\"$exp\", unit:\"$unit\", config:inputs, time:$time, run:$run }" $conf >> $out
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$conf >> $out
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done
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done
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done
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done
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done
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done
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18
overlay.nix
18
overlay.nix
@ -284,7 +284,7 @@ let
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tampi = callPackage ./garlic/exp/nbody/tampi.nix { };
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tampi = callPackage ./garlic/exp/nbody/tampi.nix { };
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# Experiment variants
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# Experiment variants
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medium = tampi.override { particles = 64 * 1024; };
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medium = tampi.override { particles = 24 * 4096; };
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baseline = medium;
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baseline = medium;
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freeCpu = baseline.override { freeCpu = true; };
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freeCpu = baseline.override { freeCpu = true; };
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jemalloc = baseline.override { enableJemalloc = true; };
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jemalloc = baseline.override { enableJemalloc = true; };
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@ -329,23 +329,31 @@ let
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timetableFromTrebuchet = tre: timetable (resultFromTrebuchet tre);
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timetableFromTrebuchet = tre: timetable (resultFromTrebuchet tre);
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mergeDatasets = callPackage ./garlic/pp/merge.nix { };
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mergeDatasets = callPackage ./garlic/pp/merge.nix { };
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# Takes a list of experiments and returns a file that contains the
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# Takes a list of experiments and returns a file that contains
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# all timetable results from the experiments.
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# all timetable results from the experiments.
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merge = exps: mergeDatasets (map timetableFromTrebuchet exps);
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merge = exps: mergeDatasets (map timetableFromTrebuchet exps);
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};
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};
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# Datasets used in the figures
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ds = with self.bsc.garlic; {
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nbody = {
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jemalloc = with exp.nbody; pp.merge [ baseline jemalloc ];
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freeCpu = with exp.nbody; pp.merge [ baseline freeCpu ];
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};
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};
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# Figures generated from the experiments
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# Figures generated from the experiments
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fig = with self.bsc.garlic; {
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fig = with self.bsc.garlic; {
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nbody = {
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nbody = {
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jemalloc = pp.rPlot {
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jemalloc = pp.rPlot {
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script = ./garlic/fig/nbody/jemalloc.R;
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script = ./garlic/fig/nbody/jemalloc.R;
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dataset = with exp.nbody; pp.merge [ baseline jemalloc ];
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dataset = ds.nbody.jemalloc;
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};
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};
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freeCpu = pp.rPlot {
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freeCpu = pp.rPlot {
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script = ./garlic/fig/nbody/freeCpu.R;
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script = ./garlic/fig/nbody/freeCpu.R;
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dataset = with exp.nbody; pp.merge [ baseline freeCpu ];
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dataset = ds.nbody.freeCpu;
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};
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};
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};
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};
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@ -359,5 +367,5 @@ in
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# Aliases
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# Aliases
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garlic = bsc.garlic;
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garlic = bsc.garlic;
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inherit (bsc.garlic) exp fig apps;
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inherit (bsc.garlic) exp fig apps ds;
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}
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}
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