Introduction

GPU acceleration is an optional feature of glmbayesCore. Matrix-level samplers — rglmb(), rlmb(), and the envelope machinery they use — run fully on the CPU regardless of whether OpenCL is available. No setup is needed for standard use.

Where GPU acceleration pays off is with large models: high-dimensional predictor sets or large posterior sample sizes. The computationally intensive work is envelope construction and evaluation — gradient and log-posterior calculations at each tangency grid point grow with model dimension and are embarrassingly parallel. Passing use_opencl = TRUE can substantially reduce wall time. See vignette("Core-08", package = "glmbayesCore") for what is accelerated and why.

End users of the formula interface (glmb(), lmb() in glmbayes) call the same backend; install and compile-time requirements below apply to glmbayesCore (see also vignette("Chapter-16", package = "glmbayes") for the glmbayes-centric checklist).

This chapter describes how to enable GPU acceleration for glmbayesCore. Ensure opencltools (and nmathopencl) are OpenCL-ready, then install glmbayesCore from source so its configure scripts can enable -DUSE_OPENCL.

What you see when you load glmbayesCore

When glmbayesCore is attached in an interactive session it checks whether GPU acceleration appears feasible. If glmbayesCore_has_opencl() is already TRUE, attach is silent.

If OpenCL was not compiled in and a GPU or OpenCL stack is detected on the host, you may see:

Note: glmbayesCore provides full CPU capability in this session
(e.g. Prior_Setup(), rglmb(), rlmb()). GPU acceleration is recommended
for bigger models and appears available. Reinstall glmbayesCore from source
with OpenCL at compile time to enable it; see vignette("Core-11",
package = "glmbayesCore") for install instructions.

On a machine with no GPU and no OpenCL stack, attach is silent.

To suppress the message in scripts or automated workflows:

options(glmbayes.quiet_opencl_startup = TRUE)

Enabling GPU acceleration: three steps

Work through these steps in order. After each step you can check whether you are done and skip the rest.

library(glmbayesCore)
glmbayesCore_has_opencl()

opencltools::has_opencl()

install.packages(
  c("opencltools", "nmathopencl"),
  repos = c("https://cloud.r-project.org", "https://knygren.r-universe.dev"),
  type = "source"
)

install.packages("devtools")

install.packages(
  "glmbayesCore",
  repos = c("https://cloud.r-project.org", "https://knygren.r-universe.dev"),
  type = "source"
)

devtools::install_github("knygren/glmbayesCore")

install.packages(
  "glmbayesCore",
  repos = c("https://cloud.r-project.org", "https://knygren.r-universe.dev"),
  type = "source"
)

library(glmbayesCore)
glmbayesCore_has_opencl()
#> [1] TRUE

Verifying the setup

diagnose_glmbayes()

The diagnostic combines opencltools host checks with compile-time status for glmbayesCore. A clean report ends with OpenCL enabled for this package (the printed label may still say “glmbayes” for historical reasons).

Common resolutions:

• Driver not installed → install or update your GPU vendor driver.
• Headers not found → install the OpenCL SDK; see opencltools Chapter 01.
• Runtime not found → install the ICD loader (ocl-icd-libopencl1 on Linux; CUDA Toolkit on Windows).
• Runtime probe failed → run clinfo outside R; check /etc/OpenCL/vendors/ on Linux.
• Package not compiled with OpenCL → repeat Step 2, then reinstall glmbayesCore from source.

Running a GPU-accelerated model

Pass use_opencl = TRUE to rglmb() (or rlmb() for Gaussian models). The Cleveland heart-disease data ship with glmbayesCore:

library(glmbayesCore)
data("Cleveland")

form <- hd ~ age + sex + cp + trestbps + chol +
  fbs + restecg + thalach + exang + oldpeak + slope + ca + thal

ps <- Prior_Setup(form, family = binomial(logit), data = Cleveland)

y <- ps$y
if (is.factor(y)) y <- as.numeric(y) - 1L else y <- as.numeric(y)

t_cpu <- system.time({
  fit_cpu <- rglmb(
    n = 20000,
    y = y,
    x = as.matrix(ps$x),
    family = binomial(link = "logit"),
    pfamily = dNormal(mu = ps$mu, Sigma = ps$Sigma),
    Gridtype = 2,
    use_parallel = TRUE,
    use_opencl = FALSE,
    verbose = FALSE
  )
})

t_gpu <- system.time({
  fit_gpu <- rglmb(
    n = 20000,
    y = y,
    x = as.matrix(ps$x),
    family = binomial(link = "logit"),
    pfamily = dNormal(mu = ps$mu, Sigma = ps$Sigma),
    Gridtype = 2,
    use_parallel = TRUE,
    use_opencl = TRUE,
    verbose = FALSE
  )
})

t_cpu
t_gpu
summary(fit_gpu)

The GPU path should match the CPU posterior up to Monte Carlo variation; gains are most visible for larger n, more predictors, and higher-dimensional grids.

Appendix A: AMD GPUs on Linux (ROCm OpenCL)

AMD provides multiple OpenCL implementations on Linux, but only ROCm OpenCL is fully supported and stable. On Ubuntu 22.04 or 24.04 LTS:

sudo apt-get install rocm-opencl-runtime

Supported AMD GPUs (ROCm) include RX 7900 / 7800 / 7700 series and Instinct MI200 / MI300. Older Polaris / Vega / Navi 1x–2x GPUs are not supported by ROCm. See vignette("Chapter-01", package = "opencltools") for full verification steps.