---
title: "Single-cell GPU Acceleration with Seurat"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{11. Single-cell GPU Acceleration with Seurat}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  # Executed locally (NOT_CRAN=true) only when Seurat is available; skipped on
  # CRAN to avoid the "CPU time > elapsed" vignette NOTE from the CPU fallback.
  eval     = identical(Sys.getenv("NOT_CRAN"), "true") &&
             requireNamespace("Seurat", quietly = TRUE) &&
             requireNamespace("SeuratObject", quietly = TRUE)
)
library(ggmlR)
if (requireNamespace("Seurat", quietly = TRUE)) {
  suppressMessages(library(Seurat))
  suppressMessages(library(SeuratObject))
}
```

ggmlR can accelerate the heavy steps of a single-cell RNA-seq pipeline on the
GPU, working **directly on `Seurat` objects**. There is no conversion on your
side and no hard dependency: `Seurat` / `SeuratObject` live in `Suggests`, so
ggmlR installs fine without them and the adapter only activates when they are
present. Vulkan is used automatically when a GPU is available, with a
transparent CPU fallback.

The adapter leans on two more `Suggests` packages for the neighbour-graph path:
`Matrix` (the sparse graphs) and `FNN` (kd-tree kNN search). Install everything
once alongside ggmlR, then just load `Seurat` — the S3 methods activate on their
own:

```r
install.packages(c("Seurat", "Matrix", "FNN"))
library(ggmlR)
library(Seurat)
```

## The one call: `RunGGML()`

`RunGGML()` mirrors Seurat's own `RunPCA()` / `NormalizeData()` style — object
in, object out, pipe-friendly. The supported operations map onto the expensive
matrix steps of a standard workflow:

| `op` | Replaces | What runs on the GPU |
|------|----------|----------------------|
| `"normalize"` | `NormalizeData()` (LogNormalize) | per-cell library-size scaling + `log1p`, elementwise |
| `"scale"` | `ScaleData()` | per-gene z-score `(x − mean) / sd` + clamp, over the full dense matrix |
| `"embed"` | `RunPCA()` | gene-by-gene covariance multiply (the eigendecomposition stays on the CPU — ggml has no eigensolver) |
| `"umap"` | `RunUMAP()` | two custom compute shaders — a tiled f32 pairwise-distance kernel and an SGD layout kernel |
| `"neighbors"` | `FindNeighbors()` | kNN distances on the GPU feeding a shared-nearest-neighbour (SNN/Jaccard) graph |

`"normalize"` and `"scale"` are **transforms**: they write the result back into
the assay (the `data` and `scale.data` layers), so the rest of the Seurat
pipeline picks them up unchanged. `"embed"` and `"umap"` add a dimensionality
reduction. `"neighbors"` writes the `<assay>_nn` / `<assay>_snn` graphs into
`@graphs`, exactly where `FindClusters()` looks.

## A worked example

We build a small synthetic object here so the vignette is self-contained; in
practice you would load your own data with `Read10X()`.

```{r}
set.seed(1)
ng <- 400L; nc <- 200L
counts <- matrix(rpois(ng * nc, lambda = 5), nrow = ng, ncol = nc)
rownames(counts) <- paste0("gene", seq_len(ng))
colnames(counts) <- paste0("cell", seq_len(nc))
counts <- methods::as(counts, "dgCMatrix")

pbmc <- CreateSeuratObject(counts = counts)
pbmc
```

### Normalize and scale on the GPU

```{r}
pbmc <- RunGGML(pbmc, op = "normalize")   # -> assay "data" layer
pbmc <- RunGGML(pbmc, op = "scale")       # -> assay "scale.data" layer
```

These behave like `NormalizeData()` and `ScaleData()` — the transformed matrices
now live in the standard assay layers:

```{r}
dim(LayerData(pbmc, layer = "data"))
dim(LayerData(pbmc, layer = "scale.data"))
```

The GPU `"normalize"` result matches Seurat's `NormalizeData()` to floating-point
tolerance:

```{r}
gpu_data <- as.matrix(LayerData(pbmc, layer = "data"))
ref_data <- as.matrix(LayerData(
  NormalizeData(pbmc, verbose = FALSE), layer = "data"))
max(abs(gpu_data - ref_data))
```

### PCA (embed)

```{r}
pbmc <- RunGGML(pbmc, op = "embed", n_components = 20, reduction_name = "ggml")
Embeddings(pbmc, "ggml")[1:3, 1:4]
```

The reduction is an ordinary `DimReduc`, so every downstream step — Seurat's own
or ggmlR's — uses it directly.

### UMAP (umap)

`op = "umap"` lays the cells out in 2-D on the GPU. Both heavy phases are custom
Vulkan compute shaders: a tiled f32 **pairwise-distance** kernel that builds the
kNN graph (it sidesteps `mul_mat`, whose f16 accumulation would reorder nearest
neighbours and corrupt the graph), and an **SGD layout** kernel that runs one
dispatch per epoch with lock-free Hogwild updates. With a kd-tree kNN search and
a sparse fuzzy graph in between, the whole UMAP runs an order of magnitude faster
than a naive reference while matching its layout to float32 precision.

```{r}
pbmc <- RunGGML(pbmc, op = "umap", reduction = "ggml", dims = 1:20,
                reduction_name = "umap")
Embeddings(pbmc, "umap")[1:3, ]
```

### Neighbour graphs (neighbors)

`op = "neighbors"` is the GPU equivalent of `FindNeighbors()`: it builds the
binary kNN graph and the shared-nearest-neighbour (SNN) graph whose weights are
the Jaccard overlap of each pair's neighbourhoods. The graphs land in `@graphs`
under Seurat's naming convention, so `FindClusters()` consumes them unchanged.

```{r}
pbmc <- RunGGML(pbmc, op = "neighbors", reduction = "ggml", dims = 1:20)
Graphs(pbmc)                                   # <assay>_nn and <assay>_snn

pbmc <- FindClusters(pbmc, graph.name = paste0(DefaultAssay(pbmc), "_snn"),
                     verbose = FALSE)
table(pbmc$seurat_clusters)
```

```{r, eval=FALSE}
DimPlot(pbmc, reduction = "umap", group.by = "seurat_clusters", label = TRUE)
```

The SNN weights are numerically identical to `FindNeighbors()` when both use the
same exact kNN (Seurat's default `Annoy` is approximate, so an exact match needs
`nn.method = "rann"`).

## Provenance

Each operation records which backend it used (and timings) in the object's
`Misc` slot, keyed by the layer / reduction it produced:

```{r}
Misc(pbmc, "data_ggml")$backend          # normalize
Misc(pbmc, "scale.data_ggml")$backend    # scale
Misc(pbmc, "ggml_ggml")$backend          # embed (and neighbors)
Misc(pbmc, "umap_ggml")$backend_sgd      # umap: layout phase backend
```

For `"umap"` the per-phase backends are reported separately
(`backend_dist` for the kNN distances, `backend_sgd` for the layout), since each
falls back to the CPU independently; the summary `backend` is `"vulkan"` only
when both ran on the GPU.

## The layers underneath

`RunGGML()` is a thin wrapper over three public generics you can also call on
their own — handy for a bare matrix with no Seurat object, or to inspect
capabilities before dispatch:

```{r}
# What can the adapter do?
names(ggml_ops_registry())
ggml_ops_registry("embed")

# Compose the layers manually on a plain matrix:
mat  <- ggml_extract(gpu_data)                       # genes x cells, dense
task <- ggml_task("embed", mat, params = list(n_components = 10))
res  <- ggml_run(task)                               # ggml_result
dim(res$embedding)                                   # cells x components
```

- `ggml_extract()` pulls a feature × cell matrix out of a `Seurat` /
  `dgCMatrix` / `matrix`, handling Seurat v4 (`GetAssayData`) vs v5
  (`LayerData`) and sparse → dense.
- `ggml_run()` validates against `ggml_ops_registry()` and routes to the Vulkan
  GPU or the CPU (`device = "auto"`, with fallback).
- `ggml_inject()` writes the result back — a reduction for `"embed"` / `"umap"`,
  an assay layer for the `"normalize"` / `"scale"` transforms, or the
  `<assay>_nn` / `<assay>_snn` graphs for `"neighbors"`.

## Bioconductor: `SingleCellExperiment`

The same `RunGGML()` works on a `SingleCellExperiment` (SCE) — the adapter has
methods for both object models. On an SCE the default assay read is
`logcounts`, results land in `reducedDim()`, transforms overwrite the named
assay, and the neighbour graphs and provenance go into `metadata()`.

```{r, eval=identical(Sys.getenv("NOT_CRAN"), "true") && requireNamespace("SingleCellExperiment", quietly = TRUE) && requireNamespace("S4Vectors", quietly = TRUE)}
library(SingleCellExperiment)

# a self-contained SCE (genes x cells), so this section does not depend on the
# Seurat object built earlier
set.seed(1)
ng <- 200L; nc <- 120L
sce_counts <- matrix(stats::rpois(ng * nc, lambda = 5), ng, nc)
rownames(sce_counts) <- paste0("gene", seq_len(ng))
colnames(sce_counts) <- paste0("cell", seq_len(nc))
sce <- SingleCellExperiment(assays = list(
  counts    = sce_counts,
  logcounts = log1p(sce_counts)))

sce <- RunGGML(sce, op = "embed", n_components = 20)         # -> reducedDim "ggml"
sce <- RunGGML(sce, op = "neighbors", reduction = "ggml", dims = 1:20)

reducedDimNames(sce)                                         # "ggml"
names(S4Vectors::metadata(sce))                              # ggml_nn / ggml_snn / ggml_ggml
```

Install the Bioconductor pieces alongside ggmlR if you work with SCE objects:

```r
BiocManager::install(c("SingleCellExperiment", "SummarizedExperiment", "S4Vectors"))
```

## A real dataset, end to end

The worked example above is synthetic so the vignette stays self-contained. A
full CPU-vs-GPU run on real data ships as an example script:

```r
system.file("examples", "seurat_op2_gpu.R", package = "ggmlR")
```

It runs the classic Seurat preprocessing route
(`percent.Largest.Gene → NormalizeData → FindVariableFeatures → ScaleData →
RunPCA → FindNeighbors → FindClusters → RunUMAP`) twice — once as stock Seurat on
the CPU, once through `RunGGML()` on the GPU — then checks that the two arms
agree numerically and prints a per-step speed-up table. The data are the Kaggle
*Open Problems – Single-Cell Perturbations* (OP2) counts: 18 211 genes ×
240 090 human PBMCs. The full matrix densifies to ~35 GB, so — as in the
reference notebook — the pipeline runs on a random subsample of the cells.

```bash
# needs Suggests: Seurat, SeuratObject, Matrix, qs2, data.table, FNN, uwot
Rscript seurat_op2_gpu.R                 # default 10% subsample
Rscript seurat_op2_gpu.R --frac 0.05     # smaller subsample
Rscript seurat_op2_gpu.R --gpu-knn       # op="neighbors" kNN on the GPU
Rscript seurat_op2_gpu.R --chunk 20000   # stream scale/PCA in blocks
Rscript seurat_op2_gpu.R --no-cpu        # GPU arm only (skip the CPU timings)
```

Every GPU step is one of the five ops from the table above; the two steps with
no GPU op (`FindVariableFeatures`, `FindClusters`) run identically in both arms.
`percent.Largest.Gene` maps to `op = "largest_gene"`, a memory-bound sparse
column argmax kept on the CPU (over the `dgCMatrix` `@x`, no densify) — the point
there is parity with `qlcMatrix::colMax`, not a GPU speed-up.

### Speed-up

A representative run — 11 % subsample (23 279 cells), 2 000 variable features, 50
PCs, `--gpu-knn` — on an RDNA-class GPU:

```
step              cpu (s)    gpu (s)   speedup
largest_gene         8.92       0.28    32.22x
normalize            2.84       1.61     1.76x
scale                1.84       1.94     0.95x
embed (PCA)         15.92       3.03     5.26x
neighbors            4.43       1.45     3.05x
umap                15.32       5.34     2.87x
TOTAL (gpu ops)     49.27      13.66     3.61x
```

The pattern is worth reading rather than just the bottom line:

- **PCA** is the biggest matrix-multiply win (5.3×) — the covariance multiply is
  exactly what the GPU is for. Its own breakdown shows where the time goes:
  `centre 0.47s  matmul_cov 1.27s  eigen 0.06s  matmul_proj 0.98s` — the
  eigensolve (CPU) is negligible, so keeping it off the GPU costs nothing.
- **`scale`** comes out ~1× (or slightly slower): it is a memory-bound
  elementwise pass with nothing to accelerate, so the host↔VRAM copy eats the
  gain. That is why `op = "scale"` defaults to the CPU even under Vulkan
  (`scale_backend = "vulkan"` forces the GPU path).
- **`umap`** here runs its kNN on the FNN kd-tree and its SGD layout on the CPU
  by default — the 2.9× comes from the GPU distance/graph phase. The SGD default
  is deliberate (see the UMAP section); `sgd_backend = "vulkan"` opts into the
  GPU shader.
- **`largest_gene`** looks like a 32× GPU win but is CPU on both sides — the
  speed-up is the sparse `@x` argmax replacing stock Seurat's `qlcMatrix` path,
  not the GPU.

### Agreement

The script densifies a fixed random column sample and compares the two arms.
Every accelerated step matches Seurat to float32-vs-float64 noise, and the
downstream clustering/embedding preserve the same structure:

```
normalize   max abs err  9.26e-07   (2000 features x 5000 sampled cells)
largest_gene top-gene agree  1.0000   percent max abs err  0.00e+00
scale       max abs err  2.38e-06
PCA         min |cor| over PC1-10  1.0000
clusters    ARI  0.9364   (11 GPU vs 11 CPU communities)
UMAP        within/total SS  gpu 0.048  cpu 0.060  (lower = tighter)
```

The comparisons account for the maths, not just the bytes: PCs are eigenvectors
so their sign is arbitrary (compare `|cor|`), clusterings are label-permuted (the
adjusted Rand index is invariant), and UMAP is stochastic and initialised
differently in each arm (so instead of coordinates, measure how tightly each
clustering separates in the embedding — the GPU layout is actually a hair tighter
here, `0.048` vs `0.060`).

## What is and isn't accelerated

Five steps of a standard workflow move to the GPU; only the final community
detection stays on the CPU:

| Standard step | ggmlR | Runs on |
|---------------|-------|---------|
| `NormalizeData()` | `RunGGML(op = "normalize")` | **GPU** |
| `ScaleData()` | `RunGGML(op = "scale")` | **GPU** |
| `RunPCA()` | `RunGGML(op = "embed")` | **GPU** matrix multiply (eigensolve on CPU) |
| `RunUMAP()` | `RunGGML(op = "umap")` | **GPU** (distance + SGD shaders) |
| `FindNeighbors()` | `RunGGML(op = "neighbors")` | **GPU** distances → CPU sparse SNN |
| `FindClusters()` | — (use Seurat's) | CPU — iterative graph Louvain/Leiden |

So a typical run is `normalize → scale → embed → umap` for visualisation and
`embed → neighbors → FindClusters` for clustering. `FindClusters` is left to
Seurat because community detection is iterative and graph-structured — a poor fit
for the GPU and already well optimised on the CPU. The PCA eigendecomposition
likewise stays on the CPU, as ggml has no eigensolver. The same operations are
available on `SingleCellExperiment` objects (see above), so the adapter covers
both the Seurat and Bioconductor ecosystems.