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CUDA

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A "Fermi" GT200 die

Hardware

NVIDIA maintains a list of supported hardware. For actual hardware, you'll need the "nvidia.ko" kernel module. 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 <Dx, Dy, Dz>, the threadId of the thread having index <x, y, z> is (x + y * Dx + z * Dy * Dx).
  • 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 <Dx, Dy>, the blockId of the block having index <x, y> is (x + y * Dx).
  • 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/Thrreg, s/Blkshmem, 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 * Thrreg or s >= b * Blkshmem.
  • 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/Fracpar 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 Blkshmem. 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 (and thus also no pipeline flushes on mispredicted branches).
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.

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
  • 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 ?

Building CUDA Apps

nvcc flags

  • -ptax-options=-v displays per-thread register usage

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 Max pitch Texalign Clock C+E? Integrated? Shared maps?
Compute capability 2.1
GeForce GTX 460 1GB 7 224 64k 48k 32k 32 1024 2G 512b 1.35GHz 2x No Yes
Compute capability 2.0
Tesla C2050 (*CB) 3GB 14 448 64k 48k 32k 32 1024 2G 512b 1.15GHz 2x No Yes
Tesla C2070 (*CB) 6GB 14 448 64k 48k 32k 32 1024 2G 512b 1.15GHz 2x 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 262144b 256b 1.30GHz Yes No Yes
GeForce GTX 295 1GB 30 240 65536b 16384b 16384 32 512 262144b 256b 1.24GHz Yes No Yes
GeForce GTX 285 1GB 30 240 65536b 16384b 16384 32 512 262144b 256b 1.48GHz Yes Yes Yes
GeForce GTX 280 1GB 30 240 65536b 16384b 16384 32 512 262144b 256b 1.30GHz Yes No Yes
GeForce GTX 260 1GB 27 216 65536b 16384b 16384 32 512 262144b 256b 1.47GHz Yes No Yes
Compute capability 1.2
GeForce GT 360M 1GB 12 96 65536b 16384b 16384 32 512 262144b 256b 1.32GHz Yes No Yes
GeForce 310 512MB 2 16 65536b 16384b 16384 32 512 262144b 256b 1.40GHz Yes No Yes
GeForce 240 GT 1GB 12 96 65536b 16384b 16384 32 512 262144b 256b 1.424GHz Yes No Yes
Compute capability 1.1
ION 256MB 2 16 65536b 16384b 8192 32 512 262144b 256b 1.1GHz No Yes Yes
Quadro FX 570 256MB 2 16 65536b 16384b 8192 32 512 262144b 256b 0.92GHz Yes No No
GeForce GTS 250 (*JR) 1G 16 128 65536b 16384b 8192 32 512 2147483647b (!) 256b 1.84GHz Yes No No
GeForce 9800 GTX 512MB 16 128 65536b 16384b 8192 32 512 262144b 256b 1.67GHz Yes Yes Yes
GeForce 9600 GT 512MB 8 64 65536b 16384b 8192 32 512 262144b 256b 1.62GHz,

1.50GHz

Yes No No
GeForce 9400M 256MB 2 16 65536b 16384b 8192 32 512 262144b 256b 0.88GHz No No No
GeForce 8800 GTS 512 512MB 16 128 65536b 16384b 8192 32 512 262144b 256b 1.62GHz Yes No No
GeForce 8600 GT 256MB 4 32 65536b 16384b 8192 32 512 262144b 256b 0.95GHz Yes No No
GeForce 9400M 512MB 1 8 65536b 16384b 8192 32 512 262144b 256b 1.40GHz No No No

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

See Also

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