ggml_backend_graph_compute_async: Compute graph asynchronously

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Compute graph asynchronously

Description

Starts graph computation without blocking. Use ggml_backend_synchronize() to wait for completion.

Usage

ggml_backend_graph_compute_async(backend, graph)

Arguments

backend	External pointer to backend
graph	External pointer to computation graph

Value

Integer status code (0 = success)

See Also

Other backend: ggml_backend_buffer_clear(), ggml_backend_buffer_get_usage(), ggml_backend_buffer_is_host(), ggml_backend_buffer_is_multi_buffer(), ggml_backend_buffer_reset(), ggml_backend_buffer_set_usage(), ggml_backend_buffer_usage_any(), ggml_backend_buffer_usage_compute(), ggml_backend_buffer_usage_weights(), ggml_backend_dev_by_name(), ggml_backend_dev_by_type(), ggml_backend_dev_count(), ggml_backend_dev_description(), ggml_backend_dev_get(), ggml_backend_dev_get_props(), ggml_backend_dev_init(), ggml_backend_dev_memory(), ggml_backend_dev_name(), ggml_backend_dev_offload_op(), ggml_backend_dev_supports_buft(), ggml_backend_dev_supports_op(), ggml_backend_dev_type(), ggml_backend_device_register(), ggml_backend_device_type_accel(), ggml_backend_device_type_cpu(), ggml_backend_device_type_gpu(), ggml_backend_device_type_igpu(), ggml_backend_event_free(), ggml_backend_event_new(), ggml_backend_event_record(), ggml_backend_event_synchronize(), ggml_backend_event_wait(), ggml_backend_get_device(), ggml_backend_graph_plan_compute(), ggml_backend_graph_plan_create(), ggml_backend_graph_plan_free(), ggml_backend_init_best(), ggml_backend_init_by_name(), ggml_backend_init_by_type(), ggml_backend_load(), ggml_backend_load_all(), ggml_backend_meta_device(), ggml_backend_multi_buffer_alloc_buffer(), ggml_backend_multi_buffer_set_usage(), ggml_backend_reg_by_name(), ggml_backend_reg_count(), ggml_backend_reg_dev_count(), ggml_backend_reg_dev_get(), ggml_backend_reg_get(), ggml_backend_reg_name(), ggml_backend_register(), ggml_backend_synchronize(), ggml_backend_tensor_copy_async(), ggml_backend_tensor_get_async(), ggml_backend_tensor_set_async(), ggml_backend_unload()

Examples

cpu <- ggml_backend_cpu_init()
ctx <- ggml_init(16 * 1024 * 1024)
a <- ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 100)
b <- ggml_relu(ctx, a)
graph <- ggml_build_forward_expand(ctx, b)
ggml_set_f32(a, rnorm(100))
# Start async computation
status <- ggml_backend_graph_compute_async(cpu, graph)
# Do other work while computation runs...
ggml_backend_synchronize(cpu)
ggml_backend_free(cpu)
ggml_free(ctx)

ggmlR documentation built on July 14, 2026, 1:08 a.m.