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90 lines
2.6 KiB
R
90 lines
2.6 KiB
R
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perf.tests.txt <- readLines("Monero-Cryptography-Benchmarks/raw-data/mock_tx_perf_z2a_1.txt")
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perf.tests.txt <- perf.tests.txt[(-1) * 1:46]
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perf.tests.txt <- perf.tests.txt[perf.tests.txt != ""]
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perf.tests.txt <- perf.tests.txt[substr(perf.tests.txt, 1, 4) != "2021"]
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i <- 2
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while (i <= length(perf.tests.txt)) {
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if ( substr(perf.tests.txt[i], 1, 12) != "test_mock_tx") {
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perf.tests.txt <- perf.tests.txt[ (-1) * i ]
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}
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i <- i + 2
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}
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test_mock_tx.data <- perf.tests.txt[grepl("^test_mock_tx", perf.tests.txt)]
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params.data <- perf.tests.txt[ ! grepl("^test_mock_tx", perf.tests.txt)]
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stopifnot(length(test_mock_tx.data) == length(params.data))
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params.data <- strsplit(params.data, " || ", fixed = TRUE)
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params.data <- lapply(params.data, FUN = function(x) {
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gsub(".+: ", "", x)
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})
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stopifnot(all(lengths(params.data) == 7))
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params.data <- as.data.frame(matrix(unlist(params.data), ncol = 7, byrow = TRUE))
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for (i in 2:7) {
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params.data[, i] <- as.numeric(params.data[, i])
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}
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colnames(params.data) <- c("type", "size.bytes", "batch.size", "inputs",
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"outputs", "rangeproof.split", "ref.set.size")
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test_mock_tx.data <- gsub(".+> +", "", test_mock_tx.data)
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test_mock_tx.data <- strsplit(test_mock_tx.data, " +")
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stopifnot(all(lengths(test_mock_tx.data) == 19))
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test_mock_tx.data <- as.data.frame(matrix(as.numeric(unlist(test_mock_tx.data)),
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ncol = 19, byrow = TRUE))
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# https://github.com/monero-project/monero/blob/master/src/common/timings.h
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colnames(test_mock_tx.data) <- c(
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"time_t",
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"size_t",
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"min",
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"max",
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"mean",
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"median",
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"stdev",
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"npskew",
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paste0("percentile.", seq(0, 100, by = 10))
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)
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combined.data <- cbind(params.data, test_mock_tx.data)
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write.csv(combined.data, file = "Monero-Cryptography-Benchmarks/processed-data/mock_tx_perf_z2a_1.csv", row.names = FALSE)
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# Very preliminary analysis starts below
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table(combined.data$type)
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summary(combined.data$stdev/combined.data$mean)
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summary(lm(mean ~ type + size.bytes + batch.size + inputs +
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outputs + rangeproof.split + ref.set.size, data = combined.data))
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combined.data.inflated <- combined.data[rep(1:nrow(combined.data), 50), ]
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summary(lm(mean ~ type + size.bytes + batch.size + inputs +
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outputs + rangeproof.split + ref.set.size, data = combined.data.inflated))
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summary(lm(log(mean) ~ type + size.bytes + batch.size + inputs +
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outputs + rangeproof.split + ref.set.size, data = combined.data.inflated))
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summary(lm(log(mean) ~ type + log(size.bytes) + log(batch.size) + log(inputs) +
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log(outputs) + log(I(rangeproof.split + 1)) + log(ref.set.size), data = combined.data.inflated))
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