CIS 665: GPU Programming
Lecture 2:
The CUDA Programming Model
1
Slides References
• Nvidia (Kitchen)
• David Kirk + Wen-Mei Hwu (UIUC)
• Gary Katz and Joe Kider
2
Fixed-function pipeline
3D API:
OpenGL or
Direct3D
3D API
Commands
3D
Application
Or Game
CPU-GPU Boundary (AGP/PCIe)
Primitive
Assembly
Pre-transformed
Fragments
Pre-transformed
Vertices
Programmable
Vertex
Processor
Programmable
Fragment
Processor
Transformed
Fragments
GPU
Front End
Pixel
Pixel
Location
Updates
Stream
Rasterization
Raster
Frame
and
Operations
Buffer
Interpolation
Assembled
Primitives
Transformed
Vertices
GPU
Command &
Data Stream
Vertex
Index
Stream
3
Why Use the GPU for Computing ?
•
The GPU has evolved into a very flexible and powerful
processor:
It’s programmable using high-level languages
It supports 32-bit floating point precision
It offers lots of GFLOPS:
GFLOPS
–
–
–
•
GPU in every PC and workstation
G80 = GeForce 8800 GTX
G71 = GeForce 7900 GTX
G70 = GeForce 7800 GTX
NV40 = GeForce 6800 Ultra
NV35 = GeForce FX 5950 Ultra
NV30 = GeForce FX 5800
4
What is Behind such an Evolution?
•
The GPU is specialized for compute-intensive, highly data
parallel computation (exactly what graphics rendering is
about)
–
So, more transistors can be devoted to data processing rather than
data caching and flow control
ALU
ALU
ALU
ALU
Control
CPU
GPU
Cache
DRAM
•
DRAM
The fast-growing video game industry exerts strong economic
pressure that forces constant innovation
5
What is (Historical) GPGPU ?
• General Purpose computation using GPU and graphics API in
applications other than 3D graphics
– GPU accelerates critical path of application
• Data parallel algorithms leverage GPU attributes
– Large data arrays, streaming throughput
– Fine-grain SIMD parallelism
– Low-latency floating point (FP) computation
• Applications – see //GPGPU.org
– Game effects (FX) physics, image processing
– Physical modeling, computational engineering, matrix algebra,
convolution, correlation, sorting
6
Previous GPGPU Constraints
• Dealing with graphics API
– Working with the corner cases of the
graphics API
Input Registers
Fragment Program
• Addressing modes
per thread
per Shader
per Context
Texture
Constants
– Limited texture size/dimension
Temp Registers
• Shader capabilities
– Limited outputs
• Instruction sets
Output Registers
FB
Memory
– Lack of Integer & bit ops
• Communication limited
– Between pixels
– Scatter a[i] = p
7
CUDA
• “Compute Unified Device Architecture”
• General purpose programming model
– User kicks off batches of threads on the GPU
– GPU = dedicated super-threaded, massively data parallel co-processor
• Targeted software stack
– Compute oriented drivers, language, and tools
• Driver for loading computation programs into GPU
–
–
–
–
–
Standalone Driver - Optimized for computation
Interface designed for compute – graphics-free API
Data sharing with OpenGL buffer objects
Guaranteed maximum download & readback speeds
Explicit GPU memory management
8
An Example of Physical Reality
Behind CUDA
CPU
(host)
GPU w/
local DRAM
(device)
9
Parallel Computing on a GPU
•
8-series GPUs deliver 25 to 200+ GFLOPS
on compiled parallel C applications
–
Available in laptops, desktops, and clusters
•
•
GPU parallelism is doubling every year
Programming model scales transparently
•
•
Programmable in C with CUDA tools
Multithreaded SPMD model uses application
data parallelism and thread parallelism
GeForce 8800
Tesla D870
Tesla S870
10
Overview
• CUDA programming model – basic concepts and
data types
• CUDA application programming interface - basic
• Simple examples to illustrate basic concepts and
functionalities
• Performance features will be covered later
11
CUDA – C with no shader limitations!
• Integrated host+device app C program
– Serial or modestly parallel parts in host C code
– Highly parallel parts in device SPMD kernel C code
Serial Code (host)
Parallel Kernel (device)
KernelA<<< nBlk, nTid >>>(args);
...
Serial Code (host)
Parallel Kernel (device)
KernelB<<< nBlk, nTid >>>(args);
...
12
CUDA Devices and Threads
•
A compute device
–
–
–
–
•
•
Is a coprocessor to the CPU or host
Has its own DRAM (device memory)
Runs many threads in parallel
Is typically a GPU but can also be another type of parallel processing
device
Data-parallel portions of an application are expressed as device
kernels which run on many threads
Differences between GPU and CPU threads
–
GPU threads are extremely lightweight
•
–
Very little creation overhead
GPU needs 1000s of threads for full efficiency
•
Multi-core CPU needs only a few
13
G80 – Graphics Mode
• The future of GPUs is programmable processing
• So – build the architecture around the processor
Host
Input Assembler
Setup / Rstr / ZCull
SP
SP
SP
TF
SP
TF
L1
SP
TF
L1
SP
SP
SP
TF
L1
L1
SP
SP
TF
L1
L2
FB
Pixel Thread Issue
SP
TF
L2
FB
SP
SP
TF
L1
L2
FB
SP
Geom Thread Issue
SP
TF
L1
L2
FB
SP
L1
L2
FB
Thread Processor
Vtx Thread Issue
L2
FB
14
G80 CUDA mode – A Device Example
• Processors execute computing threads
• New operating mode/HW interface for computing
Host
Input Assembler
Thread Execution Manager
Parallel Data
Cache
Parallel Data
Cache
Parallel Data
Cache
Parallel Data
Cache
Parallel Data
Cache
Parallel Data
Cache
Parallel Data
Cache
Parallel Data
Cache
Texture
Texture
Texture
Texture
Texture
Texture
Texture
Texture
Texture
Load/store
Load/store
Load/store
Load/store
Global Memory
Load/store
Load/store
15
Extended C
• Declspecs
– global, device, shared,
local, constant
__device__ float filter[N];
__global__ void convolve (float *image)
{
__shared__ float region[M];
...
• Keywords
– threadIdx, blockIdx
region[threadIdx] = image[i];
• Intrinsics
__syncthreads()
...
– __syncthreads
image[j] = result;
• Runtime API
– Memory, symbol,
execution management
• Function launch
}
// Allocate GPU memory
void *myimage = cudaMalloc(bytes)
// 100 blocks, 10 threads per block
convolve<<<100, 10>>> (myimage);
16
Extended C
Integrated source
(foo.cu)
cudacc
EDG C/C++ frontend
Open64 Global Optimizer
GPU Assembly
CPU Host Code
foo.s
foo.cpp
OCG
gcc / cl
G80 SASS
foo.sass
Mark Murphy, “NVIDIA’s Experience with
Open64,”
www.capsl.udel.edu/conferences/open64/2008
/Papers/101.doc
17
Arrays of Parallel Threads
• A CUDA kernel is executed by an array of
threads
– All threads run the same code (SPMD)
– Each thread has an ID that it uses to compute
memory addresses and make control decisions
threadID
0 1 2 3 4 5 6 7
…
float x = input[threadID];
float y = func(x);
output[threadID] = y;
…
18
Thread Blocks: Scalable Cooperation
• Divide monolithic thread array into multiple blocks
– Threads within a block cooperate via shared memory,
atomic operations and barrier synchronization
– Threads in different blocks cannot cooperate
Thread Block 0
Thread Block 0
threadID
0
1
2
3
4
5
6
…
float x =
input[threadID];
float y = func(x);
output[threadID] = y;
…
7
0
1
2
3
4
5
6
Thread Block N - 1
7
…
float x =
input[threadID];
float y = func(x);
output[threadID] = y;
…
0
…
1
2
3
4
5
6
7
…
float x =
input[threadID];
float y = func(x);
output[threadID] = y;
…
19
Thread Batching: Grids and Blocks
•
A kernel is executed as a grid of
thread blocks
–
•
All threads share data memory
space
Device
Grid 1
Kernel
1
A thread block is a batch of threads
that can cooperate with each other
by:
–
Synchronizing their execution
•
–
•
Host
For hazard-free shared memory
accesses
Efficiently sharing data through a
low latency shared memory
Two threads from two different
blocks cannot cooperate
Block
(0, 0)
Block
(1, 0)
Block
(2, 0)
Block
(0, 1)
Block
(1, 1)
Block
(2, 1)
Grid 2
Kernel
2
Block (1, 1)
Thread Thread Thread Thread Thread
(0, 0)
(1, 0)
(2, 0)
(3, 0)
(4, 0)
Thread Thread Thread Thread Thread
(0, 1)
(1, 1)
(2, 1)
(3, 1)
(4, 1)
Thread Thread Thread Thread Thread
(0, 2)
(1, 2)
(2, 2)
(3, 2)
(4, 2)
Courtesy: NDVIA
20
Block and Thread IDs
•
Threads and blocks have IDs
–
–
–
•
So each thread can decide what data
to work on
Block ID: 1D or 2D
Thread ID: 1D, 2D, or 3D
Device
Grid 1
Simplifies memory
addressing when processing
multidimensional data
–
–
–
Image processing
Solving PDEs on volumes
…
Block
(0, 0)
Block
(1, 0)
Block
(2, 0)
Block
(0, 1)
Block
(1, 1)
Block
(2, 1)
Block (1, 1)
Thread Thread Thread Thread Thread
(0, 0)
(1, 0)
(2, 0)
(3, 0)
(4, 0)
Thread Thread Thread Thread Thread
(0, 1)
(1, 1)
(2, 1)
(3, 1)
(4, 1)
Thread Thread Thread Thread Thread
(0, 2)
(1, 2)
(2, 2)
(3, 2)
(4, 2)
Courtesy: NDVIA
21
CUDA Device Memory Space
Overview
•
Each thread can:
–
–
–
–
–
–
•
(Device) Grid
R/W per-thread registers
R/W per-thread local memory
R/W per-block shared memory
R/W per-grid global memory
Read only per-grid constant memory
Read only per-grid texture memory
Host
The host can R/W global,
constant, and texture
memories
Block (0, 0)
Block (1, 0)
Shared Memory
Registers
Registers
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
Thread (0, 0) Thread (1, 0)
Local
Memory
Local
Memory
Local
Memory
Local
Memory
Global
Memory
Constant
Memory
Texture
Memory
22
Global, Constant, and Texture Memories
(Long Latency Accesses)
• Global memory
(Device) Grid
– Main means of communicating
R/W Data between host and
device
– Contents visible to all threads
Block (0, 0)
Block (1, 0)
Shared Memory
Registers
• Texture and Constant
Memories
– Constants initialized by host
– Contents visible to all threads
Host
Registers
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
Thread (0, 0) Thread (1, 0)
Local
Memory
Local
Memory
Local
Memory
Local
Memory
Global
Memory
Constant
Memory
Texture
Memory
23
Courtesy: NDVIA
Block IDs and Thread IDs
•
Each thread uses IDs to decide
what data to work on
–
–
•
Block ID: 1D or 2D
Thread ID: 1D, 2D, or 3D
Simplifies memory
addressing when processing
multidimensional data
–
–
–
Image processing
Solving PDEs on volumes
…
Host
Device
Grid 1
Kernel
1
Block
(0, 0)
Block
(1, 0)
Block
(0, 1)
Block
(1, 1)
Grid 2
Kernel
2
Block (1, 1)
(0,0,1) (1,0,1) (2,0,1) (3,0,1)
Thread Thread Thread Thread
(0,0,0) (1,0,0) (2,0,0) (3,0,0)
Thread Thread Thread Thread
(0,1,0) (1,1,0) (2,1,0) (3,1,0)
Courtesy: NDVIA
24
Figure 3.2. An Example of CUDA Thread Org
CUDA Memory Model Overview
• Global memory
– Main means of
communicating R/W Data
between host and device
– Contents visible to all
threads
– Long latency access
• We will focus on global
memory for now
– Constant and texture
memory will come later
Grid
Block (0, 0)
Block (1, 0)
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
Host
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
Global Memory
25
CUDA API Highlights:
Easy and Lightweight
• The API is an extension to the ANSI C programming
language
Low learning curve
• The hardware is designed to enable lightweight
runtime and driver
High performance
26
CUDA Device Memory Allocation
• cudaMalloc()
– Allocates object in the
device Global Memory
– Requires two parameters
• Address of a pointer to the
allocated object
• Size of of allocated object
• cudaFree()
Host
Grid
Block (0, 0)
Block (1, 0)
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
Global
Memory
– Frees object from device
Global Memory
• Pointer to freed object
27
CUDA Device Memory Allocation (cont.)
• Code example:
– Allocate a 64 * 64 single precision float array
– Attach the allocated storage to Md
– “d” is often used to indicate a device data structure
TILE_WIDTH = 64;
Float* Md
int size = TILE_WIDTH * TILE_WIDTH * sizeof(float);
cudaMalloc((void**)&Md, size);
cudaFree(Md);
28
CUDA Host-Device Data Transfer
• cudaMemcpy()
– memory data transfer
– Requires four parameters
•
•
•
•
Pointer to destination
Pointer to source
Number of bytes copied
Type of transfer
– Host to Host
– Host to Device
– Device to Host
– Device to Device
Grid
Block (0, 0)
Block (1, 0)
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
Host
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
Global
Memory
• Asynchronous transfer
29
CUDA Host-Device Data Transfer
(cont.)
• Code example:
– Transfer a 64 * 64 single precision float array
– M is in host memory and Md is in device memory
– cudaMemcpyHostToDevice and
cudaMemcpyDeviceToHost are symbolic constants
cudaMemcpy(Md, M, size, cudaMemcpyHostToDevice);
cudaMemcpy(M, Md, size, cudaMemcpyDeviceToHost);
30
GeForce 8800 Series Technical Specs
•
•
•
Maximum number of threads per block: 512
Maximum size of each dimension of a grid: 65,535
Number of streaming multiprocessors (SM):
–
–
•
Device memory:
–
–
•
•
•
GeForce 8800 GTX: 16 @ 675 MHz
GeForce 8800 GTS: 12 @ 600 MHz
GeForce 8800 GTX: 768 MB
GeForce 8800 GTS: 640 MB
Shared memory per multiprocessor: 16KB divided in 16 banks
Constant memory: 64 KB
Warp size: 32 threads (16 Warps/Block)
31
CUDA Keywords
32
CUDA Function Declarations
Executed
on the:
Only callable
from the:
__device__ float DeviceFunc()
device
device
__global__ void
device
host
host
host
__host__
•
KernelFunc()
float HostFunc()
__global__ defines a kernel function
– Must return void
•
__device__ and __host__ can be used
together
33
CUDA Function Declarations (cont.)
• __device__ functions cannot have their
address taken
• For functions executed on the device:
– No recursion
– No static variable declarations inside the function
– No variable number of arguments
34
Calling a Kernel Function – Thread Creation
• A kernel function must be called with an execution
configuration:
__global__ void KernelFunc(...);
dim3
DimGrid(100, 50);
// 5000 thread blocks
dim3
DimBlock(4, 8, 8);
// 256 threads per
block
size_t SharedMemBytes = 64; // 64 bytes of shared
memory
KernelFunc<<< DimGrid, DimBlock, SharedMemBytes
>>>(...);
• Any call to a kernel function is asynchronous from
CUDA 1.0 on, explicit synch needed for blocking
35
Technical details
• NVIDIA’s CUDA development tools provide three
key components to help you get started: the latest
CUDA driver, a complete CUDA Toolkit, and CUDA
SDK code samples.
• 1. CUDA Driver
• 2. CUDA Toolkit
• 3. CUDA SDK code samples
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
A Simple Running Example
Matrix Multiplication
• A simple matrix multiplication example that illustrates
the basic features of memory and thread management
in CUDA programs
–
–
–
–
–
Leave shared memory usage until later
Local, register usage
Thread ID usage
Memory data transfer API between host and device
Assume square matrix for simplicity
52
Programming Model:
Square Matrix Multiplication Example
N
WIDTH
• P = M * N of size WIDTH x WIDTH
• Without tiling:
– One thread calculates one element of P
– M and N are loaded WIDTH times from
global memory
P
WIDTH
M
WIDTH
WIDTH
53
Memory Layout of a Matrix in C
M0,0 M1,0 M2,0 M3,0
M0,1 M1,1 M2,1 M3,1
M0,2 M1,2 M2,2 M3,2
M0,3 M1,3 M2,3 M3,3
M
M0,0 M1,0 M2,0 M3,0 M0,1 M1,1 M2,1 M3,1 M0,2 M1,2 M2,2 M3,2 M0,3 M1,3 M2,3 M3,3
54
Step 1: Matrix Multiplication
A Simple Host Version in C
// Matrix multiplication on the (CPU) host in double precision
N
void MatrixMulOnHost(float* M, float* N, float* P, int Width)
{
for (int i = 0; i < Width; ++i)
j
for (int j = 0; j < Width; ++j) {
double sum = 0;
for (int k = 0; k < Width; ++k) {
double a = M[i * width + k];
double b = N[k * width + j];
M
P
sum += a * b;
}
i
P[i * Width + j] = sum;
}
}
WIDTH
WIDTH
k
k
WIDTH
WIDTH
55
Step 2: Input Matrix Data Transfer
(Host-side Code)
void MatrixMulOnDevice(float* M, float* N, float* P, int Width)
{
int size = Width * Width * sizeof(float);
float* Md, Nd, Pd;
…
1. // Allocate and Load M, N to device memory
cudaMalloc(&Md, size);
cudaMemcpy(Md, M, size, cudaMemcpyHostToDevice);
cudaMalloc(&Nd, size);
cudaMemcpy(Nd, N, size, cudaMemcpyHostToDevice);
// Allocate P on the device
cudaMalloc(&Pd, size);
56
Step 3: Output Matrix Data Transfer
(Host-side Code)
2. // Kernel invocation code – to be shown later
…
3.
// Read P from the device
cudaMemcpy(P, Pd, size, cudaMemcpyDeviceToHost);
// Free device matrices
cudaFree(Md); cudaFree(Nd); cudaFree (Pd);
}
57
Step 4: Kernel Function
// Matrix multiplication kernel – per thread code
__global__ void MatrixMulKernel(float* Md, float* Nd, float* Pd, int Width)
{
// Pvalue is used to store the element of the matrix
// that is computed by the thread
float Pvalue = 0;
58
Step 4: Kernel Function (cont.)
for (int k = 0; k < Width; ++k) {
Nd
float Melement = Md[threadIdx.y*Width+k];
float Nelement = Nd[k*Width+threadIdx.x];
Pvalue += Melement * Nelement;
tx
}
WIDTH
k
Pd[threadIdx.y*Width+threadIdx.x] = Pvalue;
Md
Pd
ty
ty
WIDTH
}
tx
k
WIDTH
WIDTH
59
Step 5: Kernel Invocation
(Host-side Code)
// Setup the execution configuration
dim3 dimGrid(1, 1);
dim3 dimBlock(Width, Width);
// Launch the device computation threads!
MatrixMulKernel<<<dimGrid, dimBlock>>>(Md, Nd, Pd, Width);
60
Only One Thread Block Used
Nd
Grid 1
• One Block of threads compute
matrix Pd
Block 1
2
4
– Each thread computes one
element of Pd
2
Thread
(2, 2)
• Each thread
– Loads a row of matrix Md
– Loads a column of matrix Nd
– Perform one multiply and
addition for each pair of Md and
Nd elements
– Compute to off-chip memory
access ratio close to 1:1 (not very
high)
• Size of matrix limited by the
number of threads allowed in a
thread block
6
3
2
5
4
48
WIDTH
Md
Pd
61
Step 7: Handling Arbitrary Sized Square
Matrices
Nd
WIDTH
• Have each 2D thread block to compute
a (TILE_WIDTH)2 sub-matrix (tile) of
the result matrix
– Each has (TILE_WIDTH)2 threads
• Generate a 2D Grid of
(WIDTH/TILE_WIDTH)2 blocks
Md
Pd
by
You still need to put a loop
around the kernel call for cases
where WIDTH/TILE_WIDTH
is greater than max grid size
(64K)!
WIDTH
TILE_WIDTH
ty
bx
WIDTH
tx
WIDTH
62
Some Useful Information on
Tools
63
Compiling a CUDA Program
C/C++ CUDA
Application
float4 me = gx[gtid];
me.x += me.y * me.z;
•
Parallel Thread
eXecution (PTX)
–
CPU Code
NVCC
–
–
PTX Code
Virtual
PhysicalPTX to Target
Compiler
G80
…
ld.global.v4.f32
mad.f32
Virtual Machine
and ISA
Programming
model
Execution
resources and
state
{$f1,$f3,$f5,$f7}, [$r9+0];
$f1, $f5, $f3, $f1;
GPU
Target code
64
64
Compilation
• Any source file containing CUDA language
extensions must be compiled with NVCC
• NVCC is a compiler driver
– Works by invoking all the necessary tools and
compilers like cudacc, g++, cl, ...
• NVCC outputs:
– C code (host CPU Code)
•
Must then be compiled with the rest of the application using another tool
– PTX
• Object code directly
• Or, PTX source, interpreted at runtime
65
65
Linking
• Any executable with CUDA code requires two
dynamic libraries:
– The CUDA runtime library (cudart)
– The CUDA core library (cuda)
66
Debugging Using the
Device Emulation Mode
• An executable compiled in device emulation mode
(nvcc -deviceemu) runs completely on the host
using the CUDA runtime
–
–
No need of any device and CUDA driver
Each device thread is emulated with a host thread
• Running in device emulation mode, one can:
–
–
–
–
Use host native debug support (breakpoints, inspection, etc.)
Access any device-specific data from host code and vice-versa
Call any host function from device code (e.g. printf) and viceversa
Detect deadlock situations caused by improper usage of
__syncthreads
67
Device Emulation Mode Pitfalls
• Emulated device threads execute sequentially, so
simultaneous accesses of the same memory location
by multiple threads could produce different results.
• Dereferencing device pointers on the host or host
pointers on the device can produce correct results in
device emulation mode, but will generate an error in
device execution mode
68
Floating Point
• Results of floating-point computations will slightly
differ because of:
– Different compiler outputs, instruction sets
– Use of extended precision for intermediate results
• There are various options to force strict single precision on the host
69