Memory management

A crucial aspect of working with a GPU is managing the data on it. The CuArray type is the primary interface for doing so: Creating a CuArray will allocate data on the GPU, copying elements to it will upload, and converting back to an Array will download values to the CPU:

# generate some data on the CPU
cpu = rand(Float32, 1024)

# allocate on the GPU
gpu = CuArray{Float32}(undef, 1024)

# copy from the CPU to the GPU
copyto!(gpu, cpu)

# download and verify
@test cpu == Array(gpu)

A shorter way to accomplish these operations is to call the copy constructor, i.e. CuArray(cpu).

Type-preserving upload

In many cases, you might not want to convert your input data to a dense CuArray. For example, with array wrappers you will want to preserve that wrapper type on the GPU and only upload the contained data. The Adapt.jl package does exactly that, and contains a list of rules on how to unpack and reconstruct types like array wrappers so that we can preserve the type when, e.g., uploading data to the GPU:

julia> cpu = Diagonal([1,2])     # wrapped data on the CPU
2×2 Diagonal{Int64,Array{Int64,1}}:
 1  ⋅
 ⋅  2

julia> using Adapt

julia> gpu = adapt(CuArray, cpu) # upload to the GPU, keeping the wrapper intact
2×2 Diagonal{Int64,CuArray{Int64,1,Nothing}}:
 1  ⋅
 ⋅  2

Since this is a very common operation, the cu function conveniently does this for you:

julia> cu(cpu)
2×2 Diagonal{Float32,CuArray{Float32,1,Nothing}}:
 1.0   ⋅
  ⋅   2.0

The cu function is opinionated and converts input most floating-point scalars to Float32. This is often a good call, as Float64 and many other scalar types perform badly on the GPU. If this is unwanted, use adapt directly.

Unified memory

The CuArray constructor and the cu function default to allocating device memory, which can be accessed only from the GPU. It is also possible to allocate unified memory, which is accessible from both the CPU and GPU with the driver taking care of data movement:

julia> cpu = [1,2]
2-element Vector{Int64}:

julia> gpu = CuVector{Int,Mem.Unified}(cpu)
2-element CuArray{Int64, 1, CUDA.Mem.UnifiedBuffer}:

julia> gpu = cu(cpu; unified=true)
2-element CuArray{Int64, 1, CUDA.Mem.UnifiedBuffer}:

Using unified memory has several advantages: it is possible to allocate more memory than the GPU has available, and the memory can be accessed efficiently from the CPU, either directly or by wrapping the CuArray using an Array:

julia> gpu[1]  # no scalar indexing error!

julia> cpu_again = unsafe_wrap(Array, gpu)
2-element Vector{Int64}:

This may make it significantly easier to port code to the GPU, as you can incrementally port parts of your application without having to worry about executing CPU code, or triggering an AbstractArray fallback. It may come at a cost however, as unified memory needs to be paged in and out of the GPU memory, and cannot be allocated asynchronously. To alleviate this cost, CUDA.jl automatically prefetches unified memory when passing it to a kernel.

On recent systems (CUDA 12.2 with the open-source NVIDIA driver) it is also possible to do the reverse, and access CPU memory from the GPU without having to explicitly allocate unified memory using the CuArray constructor or cu function:

julia> cpu = [1,2];

julia> gpu = unsafe_wrap(CuArray, cpu)
2-element CuArray{Int64, 1, CUDA.Mem.UnifiedBuffer}:

julia> gpu .+= 1;

julia> cpu
2-element Vector{Int64}:

Right now, CUDA.jl still defaults to allocating device memory, but this may change in the future. If you want to change the default behavior, you can set the default_memory preference to unified or host instead of device.

Garbage collection

Instances of the CuArray type are managed by the Julia garbage collector. This means that they will be collected once they are unreachable, and the memory hold by it will be repurposed or freed. There is no need for manual memory management, just make sure your objects are not reachable (i.e., there are no instances or references).

Memory pool

Behind the scenes, a memory pool will hold on to your objects and cache the underlying memory to speed up future allocations. As a result, your GPU might seem to be running out of memory while it isn't. When memory pressure is high, the pool will automatically free cached objects:

julia> CUDA.memory_status()             # initial state
Effective GPU memory usage: 16.12% (2.537 GiB/15.744 GiB)
Memory pool usage: 0 bytes (0 bytes reserved)

julia> a = CuArray{Int}(undef, 1024);   # allocate 8KB

julia> CUDA.memory_status()
Effective GPU memory usage: 16.35% (2.575 GiB/15.744 GiB)
Memory pool usage: 8.000 KiB (32.000 MiB reserved)

julia> a = nothing; GC.gc(true)

julia> CUDA.memory_status()             # 8KB is now cached
Effective GPU memory usage: 16.34% (2.573 GiB/15.744 GiB)
Memory pool usage: 0 bytes (32.000 MiB reserved)

If for some reason you need all cached memory to be reclaimed, call CUDA.reclaim():

julia> CUDA.reclaim()

julia> CUDA.memory_status()
Effective GPU memory usage: 16.17% (2.546 GiB/15.744 GiB)
Memory pool usage: 0 bytes (0 bytes reserved)

It should never be required to manually reclaim memory before performing any high-level GPU array operation: Functionality that allocates should itself call into the memory pool and free any cached memory if necessary. It is a bug if that operation runs into an out-of-memory situation only if not manually reclaiming memory beforehand.


If you need to disable the memory pool, e.g. because of incompatibility with certain CUDA APIs, set the environment variable JULIA_CUDA_MEMORY_POOL to none before importing CUDA.jl.

Memory limits

If you're sharing a GPU with other users or applications, you might want to limit how much memory is used. By default, CUDA.jl will configure the memory pool to use all available device memory. You can change this using two environment variables:

  • JULIA_CUDA_SOFT_MEMORY_LIMIT: This is an advisory limit, used to configure the memory pool. If you set this to a nonzero value, the memory pool will attempt to release cached memory until memory use falls below this limit. Note that this only happens at specific synchronization points, so memory use may temporarily exceed this limit. In addition, this limit is incompatible with JULIA_CUDA_MEMORY_POOL=none.
  • JULIA_CUDA_HARD_MEMORY_LIMIT: This is a hard limit, checked before every allocation. On older versions of CUDA, before v12.2, this is a relatively expensive limit, so it is recommended to first try to use the soft limit.

The value of these variables can be formatted as a numer of bytes, optionally followed by a unit, or as a percentage of the total device memory. Examples: 100M, 50%, 1.5GiB, 10000.

Avoiding GC pressure

When your application performs a lot of memory operations, the time spent during GC might increase significantly. This happens more often than it does on the CPU because GPUs tend to have smaller memories and more frequently run out of it. When that happens, CUDA invokes the Julia garbage collector, which then needs to scan objects to see if they can be freed to get back some GPU memory.

To avoid having to depend on the Julia GC to free up memory, you can directly inform CUDA.jl when an allocation can be freed (or reused) by calling the unsafe_free! method. Once you've done so, you cannot use that array anymore:

julia> a = CuArray([1])
1-element CuArray{Int64,1,Nothing}:

julia> CUDA.unsafe_free!(a)

julia> a
1-element CuArray{Int64,1,Nothing}:
Error showing value of type CuArray{Int64,1,Nothing}:
ERROR: AssertionError: Use of freed memory

Batching iterator

If you are dealing with data sets that are too large to fit on the GPU all at once, you can use CuIterator to batch operations:

julia> batches = [([1], [2]), ([3], [4])]

julia> for (batch, (a,b)) in enumerate(CuIterator(batches))
         println("Batch $batch: ", a .+ b)
Batch 1: [3]
Batch 2: [7]

For each batch, every argument (assumed to be an array-like) is uploaded to the GPU using the adapt mechanism from above. Afterwards, the memory is eagerly put back in the CUDA memory pool using unsafe_free! to lower GC pressure.