Benchmarkme

In benchmarkmeData: Data Set for the 'benchmarkme' Package

drat::addRepo("csgillespie")
rsconnect::deployApp(appDir = "~/github/benchmarkme-data/inst/shinyapps_io/",
                     account="jumpingrivers", server = "shinyapps.io",
                     appTitle = "benchmarkme"
)

library("shiny")
library("flexdashboard")
library("benchmarkmeData")
library("plotly")
data("past_results_v2", package="benchmarkmeData")
source("functions.R")

Selections {.sidebar}

Select the machines you are interested in

## The shiny part
selectInput("byte", "Byte compiled", 
            c("All", "Standard", "Optimised"))
selectInput("blas", "Blas", 
            c("All", "Standard", "Optimised"))
selectInput("os", "Operating system", 
            c("All", "Linux", "Apple", "Windows", "Unix"))

checkboxInput("is_parallel", "Parallel benchmarks", TRUE)

selectInput("test", "Benchmark test", 
            c("Programming", "Matrix functions", "Matrix calulations", 
              "Read 5MB", "Read 50MB","Read 200MB",
              "Write 5MB", "Write 50MB", "Write 200MB"))

fileInput("results", "Upload results file")

user_rank = NULL
results = reactive({
  if(!is.null(input$results)) {
    user_data = readRDS(file = input$results$datapath)
    user_results = summarise_results(user_data)
    all_results = rbind(past_results, user_results)
    user_id = unique(user_data$id)
  } else {
    all_results = past_results
    user_id = NULL
  }
  #res = select_results("prog", all_results)
  res = select_results("prog", all_results, parallel = TRUE)
  blas = get_option(input$blas)
  byte = get_option(input$byte)
  is_parallel = input$is_parallel
  res = select_results(get_test(input$test), all_results, 
                       byte_optimize = byte, 
                       blas_optimize = blas, 
                       parallel = is_parallel)
  res$is_user = FALSE
  res$is_user[res$id == user_id] = TRUE
  res = clean_table(res)

  if(input$os != "All") res = res[res$sysname == input$os,]
  colnames(res) = c("id", "Rank", "Time", "CPU",
                    "Byte Compile", "BLAS Opt", "OS", "RAM (GB)", "Test", 
                    "Cores", "is_user")

  res = res[,c(TRUE, !is_parallel, TRUE, TRUE,
               is.null(byte), is.null(blas), input$os == "All", TRUE,
               input$Test == "All",  TRUE, is_parallel, TRUE)]
  if(nrow(res) > 0) res$Rank = 1:nrow(res)
  res
})
br()

renderUI({
  ret = NULL
  user_rank = which(results()$is_user)

  if(!is.null(input$results)) {
    ret = paste(ret, "File uploaded.", br(), br())

    if(sum(user_rank) > 0) {
      ret = paste(ret, "Your machine is ranked", user_rank, "out of",
                  nrow(results()), "machines")
    } else {
      ret = paste(ret, br(), "For the current selection, your own
                machine isn't displayed")
    }
  }
  HTML(ret)
})

div(class="div-wrapper",
    a(href="http://www.jumpingrivers.com",
      img(src = "logo.png", style="width:150px;")
    )
)

Table

Top machines

DT::renderDataTable({
  r = results()
  data_table = DT::datatable(results()[,-c(1, ncol(r))], rownames=FALSE)
  if(sum(r$is_user) > 0) {
    DT::formatStyle(data_table, "Rank",
                    backgroundColor = DT::styleEqual(which(r$is_user), "orange"))
  } else {
    data_table
  }
})

Graphics

Absolute time

library(plotly)
renderPlotly({
  res = results()
  if(nrow(res) > 0) {
    res$size = res$is_user + 6
    res$size[res$is_user] =  16
    res$colour = "black"
    res$colour[res$is_user] = "steelblue"
    if(!input$is_parallel) {
      plot_ly(res, x = ~Rank, y = ~`Time`, type="scatter",
              marker = list(color = ~colour, width=10, size=~size),
              #          color= ~is_user, size=~size,
              text = ~paste("CPU: ", CPU)) %>%
        layout(yaxis = list(type = "log", title = "Time (sec)"))
    } else {
      saveRDS(res, file = "/tmp/tmp.rds")
      plot_ly(res, x = ~Cores, y = ~`Time`, type="scatter",
        marker = list(color = ~colour, width=10, size=~size),
        #          color= ~is_user, size=~size,
        text = ~paste("CPU: ", CPU)) %>%
      add_lines(color=~id) %>%
    layout(yaxis = list(type = "log", title = "Normalised time (to 1 core)"), showlegend = FALSE)



    }  
  } else {
      plot_ly()
  }

  })

Relative time

library(plotly)
renderPlotly({
  if(!input$is_parallel && nrow(results()) > 0) {
  relative = (results()$Time/min(results()$Time))
  plot_ly(results(), x = ~Rank, y = ~relative,
        text = ~paste("CPU: ", CPU)) %>%
     layout(yaxis = list(type = "log", title = "Relative time"))
  } else {
    plot_ly()
  }


})

Machine summary

Column 1

CPUs

total = function(i) sum(i > 0, na.rm = TRUE)
renderPlotly({
  i3 = total(regexpr("i3", results()$CPU))
  i5 = total(regexpr("i5", results()$CPU))
  i7 = total(regexpr("i7", results()$CPU))
  xeon = total(regexpr("Xeon", results()$CPU))
  amd = total(regexpr("AMD", results()$CPU))
  plot_ly(
    x = c(paste("Intel", c("i3", "i5", "i7", "Xeon")), "AMD"),
    y = c(i3, i5, i7, xeon, amd),
    name = "CPU distribution",
    type = "bar"
  )
})

RAM

renderPlotly({
  ram = table(results()$`RAM (GB)`)
  n = as.numeric(as.character(names(ram)))
  x_axis = factor(n)
  plot_ly(
      x = x_axis, y = ram,
      name = "CPU distribution",type = "bar"
    )
})

Operating system

total = function(i) sum(i > 0, na.rm = TRUE)
renderPlotly({
  OS = sort(table(results()$OS))
  nam = as.character(names(OS))

  plot_ly(
    x = nam,
    y = as.vector(OS),
    name = "OS distribution",
    type = "bar"
  )
})

Benchmark description

Overview

There are two main benchmarks in this package:

• benchmark_std()
  • This consists of three sub benchmarks: benchmark_prog, benchmark_matrix_cal, and benchmark_matrix_fun
• benchmark_io()
  • This consists of two sub benchmarks: read and write.

Programming benchmarks

The benchmark_prog benchmark consists of timing five matrix programming operations:

• $3,500,000$ Fibonacci numbers calculation (vector calc) - bm_prog_fib.
• Creation of a $3500 \times 3500$ Hilbert matrix (matrix calc) - bm_prog_hilbert.
• Grand common divisors of $1,000,000$ pairs (recursion) - bm_prog_gcd.
• Creation of a $1600\times 1600$ Toeplitz matrix (loops) - bm_prog_toeplitz.
• Escoufier's method on a $60\times 60$ matrix (mixed) - bm_prog_escoufier.

Matrix calulations

A collection of matrix benchmark functions aimed at assessing the calculation speed.

• Creation, transp., deformation of a $2500\times 2500$ matrix - bm_matrix_cal_manip.
• $2500\times2500$ normal distributed random matrix ^1000 - bm_matrix_cal_power.
• Sorting of $7,000,000$ random values - bm_matrix_cal_sort.
• $2500\times 2500$ cross-product matrix ($b = a' \times a$) - bm_matrix_cal_cross_product
• Linear regression over a $3000 \times 3000$ matrix - bm_matrix_cal.

Matrix functions

A collection of matrix benchmark functions

• FFT over $2,500,000$ random values - bm_matrix_fun_fft.
• Eigenvalues of a $640\times 640$ random matrix - bm_matrix_fun_eigen.
• Determinant of a $2500 \times 2500$ random matrix - bm_matrix_fun_determinant.
• Cholesky decomposition of a $3000 \times 3000$ matrix - bm_matrix_fun_cholesky.
• Inverse of a $1600 \times 1600$ random matrix - bm_matrix_fun_inverse.

Input/Output

• Reading a $5$, $50$ and $200$MB csv file
• Writing a $5$, $50$ and $200$MB csv file

The purpose of this benchmark isn't to compare write.csv to another package. Instead, we went to assess your machine.

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These benchmarks have been developed by many authors.

benchmarkmeData documentation built on April 23, 2020, 5:15 p.m.