Sexart Juniper Ren Slow Down 26022025 R Install

In a separate terminal:

# Linux/macOS
top -o CPU
# Windows
perfmon

If CPU usage is low but R is slow, it’s likely I/O or network bound.

Use the parallel package to split work across CPU cores: sexart juniper ren slow down 26022025 r install

library(parallel)
cl <- makeCluster(detectCores() - 1)
clusterExport(cl, "video_file")
parLapply(cl, 1:100, function(x) 
  # process chunk x of your video metadata
)
stopCluster(cl)

This is essential if you’re analyzing 26022025 data across many frames.

system.time(source("~/.Rprofile"))

This article documents steps to slow down Juniper REN (Rate Enforcement/Network) behavior on a Sexart-hosted server and how to install R for reproducible analytics, based on procedures applicable on 26 February 2025. It’s written for system administrators and data analysts who manage R workloads on servers using Juniper network equipment and the Sexart hosting environment. Assumptions: you have root or sudo access on the server, basic familiarity with Linux, and administrative access to the Juniper device managing your network path. In a separate terminal: # Linux/macOS top -o

If R itself (not your script) is slow after installing, check these:

| Symptom | Fix | |---------|-----| | R starts slowly | Remove unused packages from .libPaths(), reduce .Rprofile complexity. | | install.packages() takes forever | Change CRAN mirror to one close to you: chooseCRANmirror() | | dplyr/data.table are slow | Rebuild them with install.packages("data.table", type = "source") | | Memory errors | Increase memory limit: memory.limit(64000) (Windows only) | If CPU usage is low but R is

# Instead of read.csv
library(data.table)
system.time(juniper_data <- fread("26022025_data.csv"))
# fread is parallelized and memory efficient

Install av package – it’s much faster than ffmpeg bindings:

install.packages("av")
library(av)
video_info <- av_media_info("sexart_juniper_ren_slow_down_26022025.mp4")
frames <- av_video_images("sexart_juniper_ren_slow_down_26022025.mp4", 
                          format = "png", fps = 1)

This will not slow down R if you limit frames.

In a separate terminal:

# Linux/macOS
top -o CPU
# Windows
perfmon

If CPU usage is low but R is slow, it’s likely I/O or network bound.

Use the parallel package to split work across CPU cores:

library(parallel)
cl <- makeCluster(detectCores() - 1)
clusterExport(cl, "video_file")
parLapply(cl, 1:100, function(x) 
  # process chunk x of your video metadata
)
stopCluster(cl)

This is essential if you’re analyzing 26022025 data across many frames.

system.time(source("~/.Rprofile"))

This article documents steps to slow down Juniper REN (Rate Enforcement/Network) behavior on a Sexart-hosted server and how to install R for reproducible analytics, based on procedures applicable on 26 February 2025. It’s written for system administrators and data analysts who manage R workloads on servers using Juniper network equipment and the Sexart hosting environment. Assumptions: you have root or sudo access on the server, basic familiarity with Linux, and administrative access to the Juniper device managing your network path.

If R itself (not your script) is slow after installing, check these:

| Symptom | Fix | |---------|-----| | R starts slowly | Remove unused packages from .libPaths(), reduce .Rprofile complexity. | | install.packages() takes forever | Change CRAN mirror to one close to you: chooseCRANmirror() | | dplyr/data.table are slow | Rebuild them with install.packages("data.table", type = "source") | | Memory errors | Increase memory limit: memory.limit(64000) (Windows only) |

# Instead of read.csv
library(data.table)
system.time(juniper_data <- fread("26022025_data.csv"))
# fread is parallelized and memory efficient

Install av package – it’s much faster than ffmpeg bindings:

install.packages("av")
library(av)
video_info <- av_media_info("sexart_juniper_ren_slow_down_26022025.mp4")
frames <- av_video_images("sexart_juniper_ren_slow_down_26022025.mp4", 
                          format = "png", fps = 1)

This will not slow down R if you limit frames.