CUDA

A "Fermi" GT200 die

Hardware

NVIDIA maintains a list of supported hardware. You'll need the "nvidia.ko" kernel module. On Debian, use the nvidia-kernel-dkms package to build a module appropriate for your kernel (and automatically rebuild it upon kernel upgrades). You can also download the nvidia-kernel-source and nvidia-kernel-common packages, unpack /usr/src/nvidia-kernel.tar.bz2, and run make-kpkg modules_image. Install the resulting .deb, and modprobe nvidia. You'll see something like this in dmesg output:

nvidia: module license 'NVIDIA' taints kernel.
Disabling lock debugging due to kernel taint
nvidia 0000:07:00.0: enabling device (0000 -> 0003)
nvidia 0000:07:00.0: PCI INT A -> GSI 21 (level, low) -> IRQ 21
nvidia 0000:07:00.0: setting latency timer to 64
NVRM: loading NVIDIA UNIX x86_64 Kernel Module  190.53  Wed Dec  9 15:29:46 PST 2009

Once the module is loaded, CUDA should be able to find the device. See below for sample outputs. Each device has a compute capability, though this does not encompass all differentiated capabilities (see also deviceOverlap and canMapHostMemory...). Note that "emulation mode" has been removed as of CUDA Toolkit Version 3.1.

CUDA model

Host

• A host contains zero or more CUDA-capable devices (emulation must be used if zero devices are available).
• It can run multiple CUDA processes, each composed of one or more host threads.
• A given host thread can execute code on only one device at once.
• Multiple host threads can execute code on the same device.

Device

• A device packages a streaming processor array (SPA), a memory interface, and possibly memory (global memory. device memory).
  • In CUDA terminology, an integrated (vs discrete) device does not have its own global memory.
  • Specially-prepared global memory is designated constant memory, and can be cached.
• Pinned (locked) host memory avoids a bounce buffer, accelerating transfers.
  • Larger one-time setup cost due to device register programming for DMA transfers.
  • This memory will be unswappable -- allocate only as much as is needed.
• Pinned memory can be mapped directly into CUDAspace on integrated devices or in the presence of some IOMMUs.
  • "Zero (explicit)-copy" interface (can never hide all bus delays)
• Write-combining memory (configured via MTRRs or PATs) avoids PCI snoop requirements and maximizes linear throughput
  • Subtle side-effects; not to be used glibly or carelessly!
• Distributes work at block granularity to Texture Processing Clusters (TPCs).

Texture Processing Cluster

Streaming Multiprocessors (SMs) are grouped into TPCs. Each TPC contains some number of SMs and a single texture processing unit, including a few filters and a cache for texture memory. The details of these texture caches have not generally been publicized, but NVIDIA optimization guides confirm 1- and 2-dimensional spatial caching to be in effect.

Streaming Multiprocessor

• Each SM has a register file, fast local (shared) memory, a cache for constant memory, an instruction cache (ROP), a multithreaded instruction dispatcher, and some number of Stream Processors (SPs).
  • 8192 registers for compute capability <= 1.1, otherwise
  • 16384 for compute capability <= 1.3
• A group of threads which share a memory and can "synchronize their execution to coördinate accesses to memory" (use a barrier) form a block. Each thread has a threadId within its (three-dimensional) block.
  • For a block of dimensions <D_x, D_y, D_z>, the threadId of the thread having index <x, y, z> is (x + y * D_x + z * D_y * D_x).
• Register allocation is performed per-block, and rounded up to the nearest
  • 256 registers per block for compute capability <= 1.1, otherwise
  • 512 registers per block for compute capability <= 1.3.
• A group of blocks which share a kernel form a grid. Each block (and each thread within that block) has a blockId within its (two-dimensional) grid.
  • For a grid of dimensions <D_x, D_y>, the blockId of the block having index <x, y> is (x + y * D_x).
• Thus, a given thread's <blockId X threadId> dyad is unique across the grid. All the threads of a block share a blockId, and corresponding threads of various blocks share a threadId.
• Each time the kernel is instantiated, new grid and block dimensions may be provided.
• A block's threads, starting from threadId 0, are broken up into contiguous warps having some warp size number of threads.
• Distributes out-of-order work at warp granularity across SPs.
  • One program counter per warp -- divergence within warp leads to serialization.
  • Divergence is trivially supported with a per-warp stack; warps reconverge at immediate post-dominators of branches
• Supports some maximum number of blocks and threads (~8 and ~768 on G80).

Block sizing

FIXME: review/verify this!

How tightly can we bound the optimal block size T, given a warp size w? The number of threads per block ought almost always be a multiple of w, both to:

• facilitate coalescing (coalescing requirements are related to w/2), and
• maximize utilization of SPs within warp-granular scheduling.

A SM has r registers and s words of shared memory, allocated per-block (see above). Assuming that w threads can be supported (i.e., that none requires more than r/w registers or s/w words of shared memory), the most obvious lower bound is w itself. The most obvious upper bound, assuming arbitrary available work, is the greatest multiple of w supported by hardware (and, obviously, the SDK). A block must be scheduled to an SM, which requires:

• registers sufficient to support the block,
• shared memory sufficient to support the block,
• that the total number of threads not exceed some limit t (likely bounding the divergence-tracking stacks), and
• that the total number of blocks not exceed some limit b (likely bounding the warp-scheduling complexity).

A given SM, then, supports T values through the minimum of {r/Thr_reg, s/Blk_shmem, and t}; as the block requires fewer registers and less shared memory, the upper bound converges to t.

Motivations for larger blocks include:

• freedom in the b dimension exposes parallelism until t <= b * T
• larger maximum possible kernels (an absolute limit exists on grid dimensions)
• better if data can be reused among threads (e.g. in tiled matrix multiply)

Motivations for smaller blocks include:

• freedom in the t dimension exposes parallelism until t >= b * T
• freedom in the r and s dimensions exposes parallelism until r >= b * T * Thr_reg or s >= b * Blk_shmem.
• cheaper per-block operations(?) (__syncthreads(), voting, etc)
• support for older hardware and SDKs
• fairer distribution among SMs and thus possibly better utilization, lower latency
  • relative speedup tends to 0 as work grows arbitrarily on finite SMs
  • relative speedup tends to 1/Frac_par on infinitely many SMs

We can now optimize occupancy for a specific {t, b, r and s}, assuming t to be a multiple of both w and b:

• Let T = t / b. T is thus guaranteed to be the smallest multiple of w such that t == b * T.
• Check the r and w conditions. FIXME: handle reduction
• FIXME: handle very large (external) kernels

Optimizing for ranges of hardware values is left as an exercise for the reader. Occupancy is only worth optimizing if the number of warps are insufficient to hide latencies. It might be possible to eliminate latencies altogether by reusing data throughout a block via shared memory; if the algorithm permits, this is almost certainly a net win. In that case, we likely want to maximize Blk_shmem. A more advanced theory would incorporate the arithmetic intensity of a kernel...FIXME

Stream Processor

• In-order, multithreaded processor: memory latencies can be hidden only by TLP, not ILP.
  • UPDATE Vasily Volkov's awesome GTC 2010 paper, "Better Performance at Lower Occupancy", destroys this notion.
    • Really. Go read Vasily's paper. It's better than anything you'll find here.
  • Arithmetic intensity and parallelism are paramount!
  • Memory-bound kernels require sufficiently high occupancy (the ratio of concurrently-running warps to maximum possible concurrent warps (as applied, usually, to SMs)) to hide latency.
• No branch prediction or speculation. Full predication.

Memory type	PTX name	Sharing	Kernel access	Host access	Cache location	Adddressable
Registers	.reg	Per-thread	Read-write	None	None	No
Special registers	.sreg	varies	Read-only	None	None	No
Local memory	.local	Per-thread	Read-write	None	None	Yes
Shared memory	.shared	Per-block	Read-write	None	None	Yes
Global memory	.global	Global	Read-write	Read-write	1.x: None 2.0+: L1 on SM, L2 on TPC(?)	Yes
Constant memory	.const	Per-grid	Read	Read-write	Stream multiprocessor	Yes
Texture memory	.tex	Global	Read	Read-write	Texture processing cluster	texture API
Parameters (to grids or functions)	.param	Per-grid (or per-thread)	Read-only (or read-write)	None	None	Yes (or restricted)

Compute Capabilities

The original public CUDA revision was 1.0, implemented on the NV50 chipset corresponding to the GeForce 8 series. Compute capability, formed of a non-negative major and minor revision number, can be queried on CUDA-capable cards. All revisions thus far have been backwards-compatible.

}

Resource	1.0 SM	1.1 SM	1.2 SM	1.3 SM	2.0 SM	2.1 SM	3.0 SMX
CUDA cores	8	8	8	8	32	48	192
Warp schedulers	1	1	1	1	2	2	4
Insts/sched	1	1	1	1	1	2	2
Threads	768	768	1024	1024	1536	1536	2048
Warps	24	24	32	32	48	48	64
Blocks	8	8	8	8	8	8	16
32-bit registers	8192	8192	16384	16384	32768	32768	65536

Revision	Changes
1.1	Atomic ops on 32-bit global integers. Breakpoints and other debugging support.
1.2	Atomic ops on 64-bit global integers and 32-bit shared integers. 32 warps (1024 threads) and 16K registers per multiprocessor (MP). Vote instructions. Three MPs per Texture Processing Cluster (TPC). Relaxed memory coalescing constraints.
1.3	Double-precision floating point at 32 cycles per operation.
2.0	32 cores per SM 4 SFUs Atomic addition on 32-bit global and shared FP. 48 warps (1536 threads), 48K shared memory banked 32 ways, and 32K registers per MP. 512K local memory per thread. __syncthreads_{count,and,or}(), __threadfence_system(), and __ballot(). 1024 threads per block and blockIdx.{x,y} values ranging through 1024. Larger texture references. PTX 2.0 Efficient uniform addressing (ldu) Unified address space: isspacep/cvta Prefetching: prefetch/prefetchu Cache modifiers on loads and stores: .ca, .cg, .cs, .lu, .cv New integer ops: popc/clz/bfind/brev/bfe/bfi Video ops: vadd, vsub, vabsdiff, vmin, vmax, vshl, vshr, vmad, vset New special registers: nsmid, clock64, ...).
2.1	48 cores per SM 8 SFUs per SM, 8 TFUs per ROP 2 warp schedulers per SM, capable of issuing two instructions per clock
3.0	192 cores per SMX 32 SFUs per SMX, 32 TFUs per ROP 4 warp schedulers per SMX, capable of issuing two instructions per clock PTX 3.0 madc and mad.cc instructions Cubemaps and cubearrays for the tex instruction 3D surfaces via the suld.b.3d and sust.b.3d instructions pmevent.mask to trigger multiple performance counters 64-bit grid IDs 4 more performance counters, for a total of 8 DWARF debugging symbols support

PTX

Syntax Coloring

PTX with syntax coloring

I've got a vim syntax coloring file for PTX/NVIR/SASS at https://raw.github.com/dankamongmen/dankhome/master/.vim/syntax/nvir.vim. It operates by coloring all registers congruent to some integer mod 10 the same color:

syn match asmReg0	"v\?R[0-9]*0\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmReg1	"v\?R[0-9]*1\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmReg2	"v\?R[0-9]*2\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmReg3	"v\?R[0-9]*3\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmReg4	"v\?R[0-9]*4\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmReg5	"v\?R[0-9]*5\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmReg6	"v\?R[0-9]*6\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmReg7	"v\?R[0-9]*7\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmReg8	"v\?R[0-9]*8\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmReg9	"v\?R[0-9]*9\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmPReg	"P[0-9]\([0-9]*\)\(\.B\|\.F\|\.U\?\(I\|L\)\|\([^0-9]\)\@=\)"
syn match asmBB		"BB[0-9][0-9]*\(_\d\d*\)\?"
syn match asmBBNew	"BB-\d\d*"
syn match nvirNT	".NEXT_TRUE.*"
syn match nvirNF	".NEXT_FALSE.*"
syn match hexconst	"0x\x\+\(\.F\|\.U\?\(I\|L\)\)\?"
syn match spreg		"\(ctaid\|ntid\|tid\|nctaid\).\(x\|y\|z\)"

Building CUDA Apps

nvcc flags

Pass flags to ptxas via -X:

• -X -v displays per-thread register usage
• -X -abi=no disables the PTX ABI, saving registers but taking away your stack
• -dlcm={cg,cs,ca} modifies cache behavior for loads
• -dscm={cw,cs} modifies cache behavior for stores

SDK's common.mk

This assumes use of the SDK's common.mk, as recommended by the documentation.

• Add the library path to LD_LIBRARY_PATH, assuming CUDA's been installed to a non-standard directory.
• Set the CUDA_INSTALL_PATH and ROOTDIR (yeargh!) if outside the SDK.
• I keep the following in bin/cudasetup of my home directory. Source it, using sh's . cudasetup syntax:

CUDA="$HOME/local/cuda/"

export CUDA_INSTALL_PATH="$CUDA"
export ROOTDIR="$CUDA/C/common/"
if [ -n "$LD_LIBRARY_PATH" ] ; then
	export "LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA/lib64"
else
	export "LD_LIBRARY_PATH=$CUDA/lib64"
fi

unset CUDA

• Set EXECUTABLE in your Makefile, and include $CUDA_INSTALL_PATH/C/common/common.mk

Unit testing

The DEFAULT_GOAL special variable of GNU Make can be used:

.PHONY: test
.DEFAULT_GOAL:=test

include $(CUDA_INSTALL_PATH)/C/common/common.mk

test: $(TARGET)
        $(TARGET)

Libraries

Two mutually exclusive means of driving CUDA are available: the "Driver API" and "C for CUDA" with its accompanying nvcc compiler and runtime. The latter (libcudart) is built atop the former, and requires its libcuda library.

Undocumented Functions

The following unlisted functions were extracted from 3.0's libcudart.so using objdump -T:

00000000000097d0 g    DF .text	000000000000020e  Base        __cudaRegisterShared
0000000000005410 g    DF .text	0000000000000003  Base        __cudaSynchronizeThreads
0000000000009e60 g    DF .text	0000000000000246  Base        __cudaRegisterVar
000000000000a0b0 g    DF .text	0000000000000455  Base        __cudaRegisterFatBinary
00000000000095c0 g    DF .text	000000000000020e  Base        __cudaRegisterSharedVar
0000000000005420 g    DF .text	0000000000000002  Base        __cudaTextureFetch
000000000000a510 g    DF .text	00000000000009dd  Base        __cudaUnregisterFatBinary
00000000000099e0 g    DF .text	000000000000024e  Base        __cudaRegisterFunction
0000000000005820 g    DF .text	000000000000001c  Base        __cudaMutexOperation
0000000000009c30 g    DF .text	000000000000022e  Base        __cudaRegisterTexture

deviceQuery info

• Memory shown is that amount which is free; I've substituted total VRAM.
• Most CUDA devices can switch between multiple frequencies; the "Clock rate" output ought be considered accurate only at a given moment, and the outputs listed here are merely illustrative.
• Three device modes are currently supported:
  • 0: Default (multiple applications can use the device)
  • 1: Exclusive (only one application may use the device; other calls to cuCtxCreate will fail)
  • 2: Disabled (no applications may use the device; all calls to cuCtxCreate will fail
• The mode can be set using nvidia-smi's -c option, specifying the device number via -g.
• A run time limit is activated by default if the device is being used to drive a display.
• Please feel free to send me output!

Device name	Memory	MP's	Cores	Const mem	Shmem/block	Reg/block	Warp size	Thr/block	Texalign	Clock	C+E?	Integrated?	Shared maps?
Compute capability 3.0
GeForce GTX 680	1.5GB	8	1536			32	1024				Yes	No	Yes
Compute capability 2.1
GeForce GTX 460	1GB	7	224	64k	48k	32k	32	1024	512b	1.35GHz	Yes	No	Yes
Compute capability 2.0
GeForce GTX 580	1.5GB	16	512				32	1024		1.544GHz	Yes	No	Yes
Tesla C2050 (*CB)	3GB	14	448	64k	48k	32k	32	1024	512b	1.15GHz	Yes	No	Yes
Tesla C2070 (*CB)	6GB	14	448	64k	48k	32k	32	1024	512b	1.15GHz	Yes	No	Yes
GeForce GTX 480	1536MB	15	480
GeForce GTX 470	1280MB	14	448
Compute capability 1.3
Tesla C1060	4GB	30	240	65536b	16384b	16384	32	512	256b	1.30GHz	Yes	No	Yes
GeForce GTX 295	1GB	30	240	65536b	16384b	16384	32	512	256b	1.24GHz	Yes	No	Yes
GeForce GTX 285	1GB	30	240	65536b	16384b	16384	32	512	256b	1.48GHz	Yes	No	Yes
GeForce GTX 280	1GB	30	240	65536b	16384b	16384	32	512	256b	1.30GHz	Yes	No	Yes
GeForce GTX 260	1GB	27	216	65536b	16384b	16384	32	512	256b	1.47GHz	Yes	No	Yes
Compute capability 1.2
GeForce GT 360M	1GB	12	96	65536b	16384b	16384	32	512	256b	1.32GHz	Yes	No	Yes
GeForce 310	512MB	2	16	65536b	16384b	16384	32	512	256b	1.40GHz	Yes	No	Yes
GeForce 240 GT	1GB	12	96	65536b	16384b	16384	32	512	256b	1.424GHz	Yes	No	Yes
Compute capability 1.1
ION	256MB	2	16	65536b	16384b	8192	32	512	256b	1.1GHz	No	Yes	Yes
Quadro FX 570	256MB	2	16	65536b	16384b	8192	32	512	256b	0.92GHz	Yes	No	No
GeForce GTS 250 (*JR)	1G	16	128	65536b	16384b	8192	32	512	256b	1.84GHz	Yes	No	No
GeForce 9800 GTX	512MB	16	128	65536b	16384b	8192	32	512	256b	1.67GHz	Yes	Yes	Yes
GeForce 9600 GT	512MB	8	64	65536b	16384b	8192	32	512	256b	1.62GHz, 1.50GHz	Yes	No	No
GeForce 9400M	256MB	2	16	65536b	16384b	8192	32	512	256b	0.88GHz	No	No	No
GeForce 8800 GTS 512	512MB	16	128	65536b	16384b	8192	32	512	256b	1.62GHz	Yes	No	No
GeForce 8600 GT	256MB	4	32	65536b	16384b	8192	32	512	256b	0.95GHz	Yes	No	No
GeForce 9400M	512MB	1	8	65536b	16384b	8192	32	512	256b	1.40GHz	No	No	No

(*CB) Thanks to Cameron Black for this submission! (*JR) Thanks to Javier Ruiz for this submission!

See Also

• The nouveau Wiki's CUDA page
  • Honza Havlicek's guide to NVIDIA architecture
  • Context switching and the PSWITCH instruction
• The gpuocelot project, hosted on Google Code.
• The NVIDIA GPU Developer Zone
• My CUBAR tools and reverse-engineered libcudest