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gpuinfo.jl
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gpuinfo.jl
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"""
gpuinfo(deviceid::Integer)
Print out detailed information about the GPU with the given `deviceid`.
Heavily inspired by the CUDA sample "deviceQueryDrv.cpp".
"""
function gpuinfo(deviceid::Integer; io::IO=stdout)
0 <= deviceid <= ngpus() - 1 || throw(ArgumentError("Invalid device id."))
return gpuinfo(CuDevice(deviceid); io)
end
function gpuinfo(dev::CuDevice=CUDA.device(); io::IO=stdout)
# query
mp = nmultiprocessors(dev)
cores = ncudacores(dev)
max_clock_rate = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_CLOCK_RATE) ÷ 1000
mem_clock_rate = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE) ÷ 1000
mem_bus_width = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH)
l2cachesize = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_L2_CACHE_SIZE)
maxTex1D = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_WIDTH)
maxTex2D_width = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_WIDTH)
maxTex2D_height = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_HEIGHT)
maxTex3D_width = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_WIDTH)
maxTex3D_height = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_HEIGHT)
maxTex3D_depth = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE3D_DEPTH)
maxTex1DLayered_width = CUDA.attribute(
dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_WIDTH
)
maxTex1DLayered_layers = CUDA.attribute(
dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE1D_LAYERED_LAYERS
)
maxTex2DLayered_width = CUDA.attribute(
dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_WIDTH
)
maxTex2DLayered_height = CUDA.attribute(
dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_HEIGHT
)
maxTex2DLayered_layers = CUDA.attribute(
dev, CUDA.CU_DEVICE_ATTRIBUTE_MAXIMUM_TEXTURE2D_LAYERED_LAYERS
)
total_constant_mem = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_TOTAL_CONSTANT_MEMORY)
shared_mem_per_block = CUDA.attribute(
dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK
)
regs_per_block = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_REGISTERS_PER_BLOCK)
warpsize = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_WARP_SIZE)
max_threads_per_mp = CUDA.attribute(
dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_MULTIPROCESSOR
)
max_threads_per_block = CUDA.attribute(
dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK
)
blockdim_x = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_X)
blockdim_y = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Y)
blockdim_z = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_BLOCK_DIM_Z)
griddim_x = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_X)
griddim_y = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Y)
griddim_z = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_GRID_DIM_Z)
texture_align = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_TEXTURE_ALIGNMENT)
max_mem_pitch = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MAX_PITCH)
async_engine_count = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_ASYNC_ENGINE_COUNT)
gpu_overlap = Bool(CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_GPU_OVERLAP))
kernel_exec_timeout_enabled = Bool(
CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_KERNEL_EXEC_TIMEOUT)
)
integrated = Bool(CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_INTEGRATED))
can_map_host_mem = Bool(
CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_CAN_MAP_HOST_MEMORY)
)
concurrent_kernels = Bool(
CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_CONCURRENT_KERNELS)
)
surface_alignment = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_SURFACE_ALIGNMENT) > 0
ecc_enabled = Bool(CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_ECC_ENABLED))
unified_addressing = Bool(
CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING)
)
managed_memory = Bool(CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MANAGED_MEMORY))
compute_preemption = Bool(
CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_COMPUTE_PREEMPTION_SUPPORTED)
)
cooperative_launch = Bool(
CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_COOPERATIVE_LAUNCH)
)
cooperative_multi_dev_launch = Bool(
CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_COOPERATIVE_MULTI_DEVICE_LAUNCH)
)
pci_domainid = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_PCI_DOMAIN_ID)
pci_busid = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_PCI_BUS_ID)
pci_deviceid = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_PCI_DEVICE_ID)
compute_mode = CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_COMPUTE_MODE)
comp_modes = [
"Default (multiple host threads can use ::cudaSetDevice() with device simultaneously)",
"Exclusive (only one host thread in one process is able to use ::cudaSetDevice() with this device)",
"Prohibited (no host thread can use ::cudaSetDevice() with this device)",
"Exclusive Process (many threads in one process is able to use ::cudaSetDevice() with this device)",
"Unknown",
]
# printing
println(io, "Device: ", name(dev), " ($dev)")
println(
io, "Total amount of global memory: ", Base.format_bytes(Int(CUDA.totalmem(dev)))
)
println(io, "Number of CUDA cores: ", cores)
println(io, "Number of multiprocessors: ", mp, " ($(cores ÷ mp) CUDA cores each)")
println(io, "GPU max. clock rate: ", max_clock_rate, " MHz")
println(io, "Memory clock rate: ", mem_clock_rate, " MHz")
println(io, "Memory bus width: ", mem_bus_width, "-bit")
println(io, "L2 cache size: ", Base.format_bytes(l2cachesize))
println(io, "Max. texture dimension sizes (1D): $maxTex1D")
println(io, "Max. texture dimension sizes (2D): $maxTex2D_width, $maxTex2D_height")
println(
io,
"Max. texture dimension sizes (3D): $maxTex3D_width, $maxTex3D_height, $maxTex3D_depth",
)
println(
io,
"Max. layered 1D texture size: $(maxTex1DLayered_width) ($(maxTex1DLayered_layers) layers)",
)
println(
io,
"Max. layered 2D texture size: $(maxTex2DLayered_width), $(maxTex2DLayered_height) ($(maxTex2DLayered_layers) layers)",
)
println(io, "Total amount of constant memory: ", Base.format_bytes(total_constant_mem))
println(
io,
"Total amount of shared memory per block: ",
Base.format_bytes(shared_mem_per_block),
)
println(io, "Total number of registers available per block: ", regs_per_block)
println(io, "Warp size: ", warpsize)
println(io, "Max. number of threads per multiprocessor: ", max_threads_per_mp)
println(io, "Max. number of threads per block: ", max_threads_per_block)
println(
io,
"Max. dimension size of a thread block (x,y,z): $(blockdim_x), $(blockdim_y), $(blockdim_z)",
)
println(
io,
"Max. dimension size of a grid size (x,y,z): $(griddim_x), $(griddim_y), $(griddim_z)",
)
println(io, "Texture alignment: ", Base.format_bytes(texture_align))
println(io, "Maximum memory pitch: ", Base.format_bytes(max_mem_pitch))
println(
io,
"Concurrent copy and kernel execution: ",
gpu_overlap ? "Yes" : "No",
" with $(async_engine_count) copy engine(s)",
)
println(io, "Run time limit on kernels: ", kernel_exec_timeout_enabled ? "Yes" : "No")
println(io, "Integrated GPU sharing host memory: ", integrated ? "Yes" : "No")
println(
io, "Support host page-locked memory mapping: ", can_map_host_mem ? "Yes" : "No"
)
println(io, "Concurrent kernel execution: ", concurrent_kernels ? "Yes" : "No")
println(io, "Alignment requirement for surfaces: ", surface_alignment ? "Yes" : "No")
println(io, "Device has ECC support: ", ecc_enabled ? "Yes" : "No")
println(
io, "Device supports Unified Addressing (UVA): ", unified_addressing ? "Yes" : "No"
)
println(io, "Device supports managed memory: ", managed_memory ? "Yes" : "No")
println(io, "Device supports compute preemption: ", compute_preemption ? "Yes" : "No")
println(io, "Supports cooperative kernel launch: ", cooperative_launch ? "Yes" : "No")
println(
io,
"Supports multi-device co-op kernel launch: ",
cooperative_multi_dev_launch ? "Yes" : "No",
)
println(
io,
"Device PCI domain ID / bus ID / device ID: $(pci_domainid) / $(pci_busid) / $(pci_deviceid)",
)
println(io, "Compute mode: ", comp_modes[compute_mode + 1])
return nothing
end
"""
Query peer-to-peer (i.e. inter-GPU) access support.
"""
function gpuinfo_p2p_access(; io::IO=stdout)
# check p2p access
ndevs = ngpus()
if ndevs <= 1
println(io, "Only a single GPU available.")
else
mat_p2p_access_supported = Matrix{Bool}(undef, ndevs, ndevs)
mat_p2p_can_access = Matrix{Bool}(undef, ndevs, ndevs)
mat_p2p_atomic_supported = Matrix{Bool}(undef, ndevs, ndevs)
for i in 1:ndevs
dev_i = CuDevice(i - 1)
for j in 1:ndevs
dev_j = CuDevice(j - 1)
if i != j
p2p_access_supported = Bool(
CUDA.p2p_attribute(
dev_i, dev_j, CUDA.CU_DEVICE_P2P_ATTRIBUTE_ACCESS_SUPPORTED
),
)
p2p_can_access = Bool(CUDA.can_access_peer(dev_i, dev_j))
p2p_atomic_supported = Bool(
CUDA.p2p_attribute(
dev_i,
dev_j,
CUDA.CU_DEVICE_P2P_ATTRIBUTE_NATIVE_ATOMIC_SUPPORTED,
),
)
mat_p2p_atomic_supported[i, j] = p2p_atomic_supported
mat_p2p_access_supported[i, j] = p2p_access_supported
mat_p2p_can_access[i, j] = p2p_can_access
# p2p_performance_rank = CUDA.p2p_attribute(dev_i, dev_j, CUDA.CU_DEVICE_P2P_ATTRIBUTE_PERFORMANCE_RANK)
else
mat_p2p_atomic_supported[i, i] = false
mat_p2p_access_supported[i, i] = false
mat_p2p_can_access[i, j] = false
end
end
end
printstyled(io, "P2P Access Supported:\n"; bold=true)
show(io, "text/plain", mat_p2p_access_supported)
println(io)
println(io)
if mat_p2p_access_supported != mat_p2p_can_access
printstyled(io, "P2P Can Access:\n"; bold=true)
show(io, "text/plain", mat_p2p_can_access)
println(io)
println(io)
end
printstyled(io, "P2P Atomic Supported:\n"; bold=true)
show(io, "text/plain", mat_p2p_atomic_supported)
println(io)
println(io)
end
return nothing
end
ngpus() = length(CUDA.devices())
function nmultiprocessors(dev::CuDevice=CUDA.device())
return CUDA.attribute(dev, CUDA.CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT)
end
ncudacores(deviceid::Integer) = ncudacores(CuDevice(deviceid))
function ncudacores(device::CuDevice=CUDA.device())
capver = CUDA.capability(device)
return ncudacores(capver.major, capver.minor, nmultiprocessors(device))
end
function ncudacores(major, minor, mp)
# based on https://stackoverflow.com/questions/32530604/how-can-i-get-number-of-cores-in-cuda-device
# helper_cuda_drvapi provides something like https://github.com/LinkedInAttic/datacl/blob/master/approxalgos/GPU_Work_Final2/bussAnal/filter/lib/helper_cuda_drvapi.h#L82 but is header only
cores = 0
err_msg = "Unknown device type / compute capability version (major $major, minor $minor)"
if major == 2 # Fermi
if minor == 1
cores = mp * 48
else
cores = mp * 32
end
elseif major == 3 # Kepler
cores = mp * 192
elseif major == 5 # Maxwell
cores = mp * 128
elseif major == 6 # Pascal
if (minor == 1) || (minor == 2)
cores = mp * 128
elseif minor == 0
cores = mp * 64
else
error(err_msg)
end
elseif major == 7 # Volta and Turing
if (minor == 0) || (minor == 5)
cores = mp * 64
else
error(err_msg)
end
elseif major == 8 # Ampere and Ada Lovelace
if minor == 0
cores = mp * 64
elseif minor == 6
cores = mp * 128
elseif minor == 9
cores = mp * 128
else
error(err_msg)
end
elseif major == 9 # Hopper
if minor == 0
cores = mp * 128
else
error(err_msg)
end
else
error(err_msg)
end
return cores
end
function ntensorcores(device::CuDevice=CUDA.device())
capver = CUDA.capability(device)
return ntensorcores(capver.major, capver.minor, nmultiprocessors(device))
end
function ntensorcores(major, minor, mp)
# based on https://en.wikipedia.org/wiki/CUDA
err_msg = "Unknown device type / compute capability version (major $major, minor $minor)"
if major == 7
if minor in (0, 2, 5)
return 8 * mp
else
error(err_msg)
end
elseif major == 8 # Ampere and Ada Lovelace
if minor in (0, 6, 7, 9)
return 4 * mp
else
error(err_msg)
end
elseif major == 9 # Hopper
if minor == 0
return 4 * mp
else
error(err_msg)
end
elseif major < 7
return 0
else
error(err_msg)
end
end
"""
List the available GPUs.
"""
function gpus(; io::IO=stdout)
# Based on https://github.com/JuliaGPU/CUDA.jl/blob/ca77d1828f3bc0df34501de848c7a13f1df0b1fe/src/utilities.jl#L69
devs = devices()
if isempty(devs)
println(io, "No CUDA-capable devices.")
elseif length(devs) == 1
println(io, "1 device:")
else
println(io, length(devs), " devices:")
end
for (i, dev) in enumerate(devs)
if has_nvml()
mig = uuid(dev) != parent_uuid(dev)
nvml_gpu = NVML.Device(parent_uuid(dev))
nvml_dev = NVML.Device(uuid(dev); mig)
str = NVML.name(nvml_dev)
cap = NVML.compute_capability(nvml_gpu)
mem = NVML.memory_info(nvml_dev)
else
str = name(dev)
cap = capability(dev)
mem = device!(dev) do
# this requires a device context, so we prefer NVML
(free=available_memory(), total=total_memory())
end
end
println(
io,
" $(i-1): $str (sm_$(cap.major)$(cap.minor), $(Base.format_bytes(mem.free)) / $(Base.format_bytes(mem.total)) available)",
)
end
end