# Chapter 12: The nmathopencl R API — Distribution Functions on the GPU

#### Kjell Nygren

#### 2026-06-11

## Overview

`nmathopencl` exports a family of R functions that mirror R’s `stats::` distribution functions but dispatch computation to an OpenCL device when one is available. The calling convention is intentionally close to the base-R equivalents so that substitution is straightforward.

All exported functions share three common arguments:

Argument | Type | Default | Meaning
--- | --- | --- | ---
`fallback` | logical | `FALSE` | If `TRUE` while `nmathopencl_has_opencl()` is `TRUE`, fall back to the `stats::` analogue when the OpenCL call fails; ignored when OpenCL is unavailable (CPU is used anyway)
`verbose` | logical | `FALSE` | Extra diagnostics (including when picking the CPU path with no OpenCL runtime)
`log` / `lower.tail` | logical | distribution-specific | Mirror the `stats::` convention for the same function

## Checking availability

    library(nmathopencl)

    # TRUE if this nmathopencl build was compiled with USE_OPENCL
    nmathopencl_has_opencl()

    # GPU device names on the host (opencltools --- system inventory)
    opencltools::gpu_names()

If OpenCL is not available (`nmathopencl_has_opencl()` returns `FALSE`), calls use the CPU equivalent unconditionally so examples and CI keep working.

By default `fallback = FALSE`: when OpenCL is available but a dispatch fails, the error propagates—useful while debugging kernels. Pass `fallback = TRUE` only when you deliberately want GPU failures masked with the CPU analogue.

## Normal distribution

    x  <- rnorm(1e6)

    # Density
    d  <- dnorm_opencl(x, mean = 0, sd = 1, log = FALSE)

    # CDF --- same core arguments as stats::pnorm(q, mean, sd, lower.tail, log.p);
    #          plus opencl_parallel, fallback, verbose. Long outputs use recycling length.
    p <- pnorm_opencl(
      rep(1.96, 1e6),
      mean = 0,
      sd = 1,
      lower.tail = TRUE,
      log.p = FALSE
    )

    # Quantile (`qnorm_opencl` retains leading `n` + scalar `p`, unlike `stats::qnorm`-style vectors)
    q  <- qnorm_opencl(n = 1e6, p = 0.975, mean = 0, sd = 1)

    # Random draws
    r  <- rnorm_opencl(n = 1e6, mean = 0, sd = 1)

Note the signature difference between `dnorm_opencl` (takes a vector `x`) and `qnorm_opencl` / `rnorm_opencl`, which retain a leading `n` argument for replicate-many quantiles (`qnorm`) or draws (`rnorm`). For the CDF, `pnorm_opencl` parallels `stats::pnorm(q, mean = 0, sd = 1, lower.tail = TRUE, log.p = FALSE)` and adds only `opencl_parallel`, `fallback`, and `verbose`. Output length is the usual recycled length (`max(length(q), ...)`, as in stats). On GPU (when used), each output element runs `pnorm_kernel` once in sequence (high overhead until a batched kernel is added).

## Distribution families

### Gamma

    dgamma_opencl(n, x, shape = 2, scale = 1)
    pgamma_opencl(q, shape = 2, rate = 1)
    rgamma_opencl(n, shape = 2, scale = 1)
    # qgamma: use stats::qgamma(); the OpenCL kernel is internal-only
    # (device compile failure, see inst/OPENCL_KERNEL_KNOWN_FAILURES.md)

### Binomial

    dbinom_opencl(x, size = 10, prob = 0.3)
    pbinom_opencl(q, size = 10, prob = 0.3)
    qbinom_opencl(p, size = 10, prob = 0.3)
    rbinom_opencl(n, size = 10, prob = 0.3)

### Poisson

    dpois_opencl(x, lambda = 3)
    ppois_opencl(q, lambda = 3)
    qpois_opencl(p, lambda = 3)
    rpois_opencl(n, lambda = 3)

### Beta

    dbeta_opencl(x, shape1 = 2, shape2 = 5)
    pbeta_opencl(q, shape1 = 2, shape2 = 5)
    rbeta_opencl(n, shape1 = 2, shape2 = 5)
    # qbeta: use stats::qbeta(); the OpenCL kernel is internal-only
    # (device link failure, see inst/OPENCL_KERNEL_KNOWN_FAILURES.md)

### Additional families

All families below follow the same four-function pattern (`d*`, `p*`, `q*`, `r*`) with the same `fallback` / `verbose` arguments:

Family | R file
--- | ---
Cauchy | `cauchy_opencl.R`
Chi-squared | `chisq_opencl.R`
Exponential | `exponential_opencl.R`
F | `f_opencl.R`
Geometric | `geometric_opencl.R`
Hypergeometric | `hypergeometric_opencl.R`
Log-normal | `lnorm_opencl.R`
Logistic | `logistic_opencl.R`
Negative binomial | `negative_binomial_opencl.R`
t | `t_opencl.R`
Uniform | `uniform_opencl.R`
Weibull | `weibull_opencl.R`
Multivariate discrete | `multinomial_opencl.R`
Tukey | `tukey_opencl.R`

The Wilcoxon rank-sum (`rwilcox_opencl.R`), Wilcoxon signed-rank (`signrank_opencl.R`), and Bessel (`bessel_opencl.R`) wrappers are internal-only: their OpenCL ports have documented allocation/runtime failures (see `inst/OPENCL_KERNEL_KNOWN_FAILURES.md` and the notes in `inst/cl/`). Use the `stats`/`base` equivalents instead.

### Noncentral distributions

Noncentral variants (`dnt_opencl`, `dnchisq_opencl`, `dnf_opencl`, `dnbeta_opencl`, and their `p*`, `q*` counterparts) are available with the noncentrality parameter `ncp`.

## Special functions and math support

    # Log-gamma
    lgammafn_opencl(n, x)
    lgammafn_sign_opencl(n, x)

    # Gamma function
    gammafn_opencl(n, x)

    # Log-gamma at x+1
    lgamma1p_opencl(n, x)

    # Digamma / polygamma
    digamma_opencl(n, x)
    trigamma_opencl(n, x)
    psigamma_opencl(n, x, deriv)

    # Beta and log-beta
    beta_special_opencl(n, a, b)
    lbeta_special_opencl(n, a, b)

    # Binomial coefficients
    choose_special_opencl(n_out, n, k)
    lchoose_special_opencl(n_out, n, k)

    # Logspace arithmetic
    logspace_add_opencl(n, logx, logy)
    logspace_sub_opencl(n, logx, logy)
    logspace_sum_opencl(n, logx, logy)

    # Math support
    fmax2_opencl(n, x, y)
    fmin2_opencl(n, x, y)

These are exported via `special_opencl.R` and `math_support_opencl.R`.

## R extension utilities (`R_ext`)

    # R-compatible power
    r_pow_opencl(n, x, y)
    r_pow_di_opencl(n, x, i)

    # Miscellaneous math helpers
    log1pmx_opencl(n, x)
    log1pexp_opencl(n, x)
    log1mexp_opencl(n, x)
    pow1p_opencl(n, x, y)

These are exported via `rext_utils_opencl.R`.

## RNG core

    # Base RNG draws
    norm_rand_opencl(n)
    unif_rand_opencl(n)
    exp_rand_opencl(n)
    r_unif_index_opencl(n, dt)

These generate random samples using device-side implementations of the same Kinderman-Ramage / Ahrens-Dieter algorithms used by R’s internal RNG. They are exported via `rng_core_opencl.R`.

## Fallback behavior in detail

1. `nmathopencl_has_opencl()` is `FALSE` (no runtime device / not compiled with OpenCL): the wrapper always uses the CPU `stats::`/base analogue. The `fallback` argument does not gate this; it is ignored for this branch.

2. `nmathopencl_has_opencl()` is `TRUE`, but OpenCL enqueue/compile/bind fails inside the `.Call`: the outer R shim catches the failure. `fallback = FALSE` (default): propagate the error. `fallback = TRUE`: return the CPU analogue instead (optional masking for fragile stacks).

Runnable examples under `inst/examples/` mostly use `fallback = FALSE` so `R CMD check` surfaces real OpenCL breakage when a device is present.

    # Default --- surface GPU/build errors during development / local check with OpenCL enabled:
    dnorm_opencl(x)

    # Permit CPU masking while OpenCL is flaky (not the development default):
    dnorm_opencl(x, fallback = TRUE, verbose = TRUE)

## Performance notes

GPU acceleration is most beneficial for large vectors (>= ~10,000 elements) and for functions with non-trivial per-element computation (e.g., `pgamma`, `pt`, `qnorm`). For very small inputs the kernel compilation overhead and data transfer latency dominate.

The first call in an R session may be slightly slower because OpenCL JIT- compiles the kernel source for the specific device. Subsequent calls with the same kernel reuse the compiled program from the driver’s cache.