Transcript GPU History CUDA Intro Graphics Pipeline Elements 1. A scene description: vertices, triangles, colors, lighting 2.Transformations that map the scene to a camera viewpoint 3.“Effects”: texturing,

GPU History
CUDA Intro
Graphics Pipeline Elements
1. A scene description: vertices, triangles, colors,
lighting
2.Transformations that map the scene to a
camera viewpoint
3.“Effects”: texturing, shadow mapping, lighting
calculations
4.Rasterizing: converting geometry into pixels
5.Pixel processing: depth tests, stencil tests, and
other per-pixel operations.
CPU
Host
Host Interface
Vertex Control
GPU
Vertex
Cache
VS/T&L
A Fixed Function
GPU Pipeline
Triangle Setup
Raster
Shader
ROP
FBI
Texture
Cache
Frame
Buffer
Memory
Texture Mapping Example
Texture mapping example: painting a world map
texture image onto a globe object.
Programmable Vertex and Pixel Processors
3D Application
or Game
3D API
Commands
CPU
3D API:
OpenGL or
Direct3D
CPU – GPU Boundary
GPU
Command &
Data Stream
GPU
Front
End
GPU
Assembled
Polygons,
Lines, and
Points
Vertex Index
Stream
Primitive
Assembly
Pre-transformed
Vertices
Pixel
Location
Stream
Rasterization &
Interpolation
Rasterized
Transformed Pre-transformed
Vertices
Fragments
Programmable
Vertex
Processor
Pixel
Updates
Raster
Ops
Framebuffer
Transformed
Fragments
Programmable
Fragment
Processor
An example of separate vertex processor and fragment processor in
a programmable graphics pipeline
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
– Model is SPMD
– Low-latency floating point (FP) computation
• Applications – see http://gpgpu.org
– Game effects (FX) physics, image processing
– Physical modeling, computational engineering, matrix algebra,
convolution, correlation, sorting
Tesla GPU
• NVIDIA developed a more general purpose GPU
• Can programming it like a regular processor
• Must explicitly declare the data parallel parts of the
workload
– Shader processors  fully programming processors with
instruction memory, cache, sequencing logic
– Memory load/store instructions with random byte
addressing capability
– Parallel programming model primitives; threads, barrier
synchronization, atomic operations
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
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
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
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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;
…
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
– Up to 65535 blocks, 512 threads/block
Thread Block 1
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;
…
Block IDs and Thread IDs
•
•
We launch a “grid” of “blocks”
of “threads”
Each thread uses IDs to decide
what data to work on
–
–
•
Image processing
Solving PDEs on volumes
…
Device
Grid 1
Kernel
1
Block ID: 1D or 2D
Thread ID: 1D, 2D, or 3D
Simplifies memory
addressing when processing
multidimensional data
–
–
–
Host
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
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
Grid
Block (0, 0)
Block (1, 0)
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
Host
Global Memory
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
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 allocated object
• cudaFree()
Host
– Frees object from device
Global Memory
• Pointer to freed object
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
DON’T use a CPU
pointer in a GPU
function !
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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);
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
• Non-blocking/asynchronous
transfer
Global
Memory
Shared Memory
Registers
Registers
Thread (0, 0) Thread (1, 0)
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);
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
Code Example
__global__ void add(int a, int b, int *c)
{
*c = a + b;
}
int main()
{
int a,b,c;
int *dev_c;
a=3;
b=4;
cudaMalloc((void**)&dev_c, sizeof(int));
add<<<1,1>>>(a,b,dev_c); // 1 Block and 1 Thread/Block
cudaMemcpy(&c, dev_c, sizeof(int), cudaMemcpyDeviceToHost);
printf("%d + %d is %d\n", a, b, c);
cudaFree(dev_c);
return 0;
}
Sequential Code – Adding Arrays
#define N 10
void add(int *a, int *b, int *c)
{
int tID = 0;
while (tID < N)
{
c[tID] = a[tID] + b[tID];
tID += 1;
}
}
int main()
{
int a[N], b[N], c[N];
// Fill Arrays
for (int i = 0; i < N; i++)
{
a[i] = i,
b[i] = 1;
}
add (a, b, c);
for (int i = 0; i < N; i++)
{
printf("%d + %d = %d\n", a[i], b[i], c[i]);
}
return 0;
}
CUDA Code – Adding Arrays
#include "stdio.h"
#define N 10
int main()
{
int a[N], b[N], c[N];
int *dev_a, *dev_b, *dev_c;
__global__ void add(int *a, int *b, int *c)
{
int tID = blockIdx.x;
if (tID < N)
{
c[tID] = a[tID] + b[tID];
}
}
cudaMalloc((void **) &dev_a, N*sizeof(int));
cudaMalloc((void **) &dev_b, N*sizeof(int));
cudaMalloc((void **) &dev_c, N*sizeof(int));
// Fill Arrays
for (int i = 0; i < N; i++)
{
a[i] = i,
b[i] = 1;
}
cudaMemcpy(dev_a, a, N*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, N*sizeof(int), cudaMemcpyHostToDevice);
add<<<N,1>>>(dev_a, dev_b, dev_c);
cudaMemcpy(c, dev_c, N*sizeof(int), cudaMemcpyDeviceToHost);
for (int i = 0; i < N; i++)
{
printf("%d + %d = %d\n", a[i], b[i], c[i]);
}
return 0;
}
Julia Fractal
• Evaluates an iterative equation
for points in the complex plane
– A point is not in the set if iterating
diverges and approaches infinity
– A point is in the set if iterating
remains bounded
• Equation
– Zn+1=Zn2 + C
• Where Z is a point in the complex
plane, C is a constant
• Our implementation uses the
freeimage library
CPU Fractal Implementation
• Structure to store, multiply, and divide complex
numbers
#include "FreeImage.h"
#include "stdio.h"
#define DIM 1000
struct cuComplex {
float r;
float i;
cuComplex( float a, float b ) : r(a), i(b) {}
float magnitude2( void ) { return r * r + i * i; }
cuComplex operator*(const cuComplex& a) {
return cuComplex(r*a.r - i*a.i, i*a.r + r*a.i);
}
cuComplex operator+(const cuComplex& a) {
return cuComplex(r+a.r, i+a.i);
}
};
CPU Fractal Implementation
• Julia function
int julia(int x, int y)
{
const float scale = 1.5;
float jx = scale * (float)(DIM/2 - x)/(DIM/2);
float jy = scale * (float)(DIM/2 - y)/(DIM/2);
cuComplex c(-0.8, 0.156);
cuComplex a(jx, jy);
int i = 0;
for (i = 0; i < 200; i++)
{
a = a*a + c;
if (a.magnitude2() > 1000) return 0;
}
return 1;
}
CPU Fractal Implementation
• What will become our kernel
– Array of char is 0 or 1 to indicate pixel or no pixel
void kernel(char *ptr)
{
for (int y = 0; y<DIM; y++)
for (int x=0; x<DIM; x++)
{
int offset = x + y * DIM;
ptr[offset] = julia(x,y);
}
}
CPU Fractal Implementation
int main()
{
FreeImage_Initialise();
FIBITMAP * bitmap = FreeImage_Allocate(DIM, DIM, 32);
char charmap[DIM][DIM];
kernel(&charmap[0][0]);
RGBQUAD color;
for (int i = 0; i < DIM; i++){
for (int j = 0; j < DIM; j++){
color.rgbRed = 0;
color.rgbGreen = 0;
color.rgbBlue = 0;
if (charmap[i][j]!=0)
color.rgbBlue = 255;
FreeImage_SetPixelColor(bitmap, i, j, &color);
}
}
FreeImage_Save(FIF_BMP, bitmap, "output.bmp");
FreeImage_Unload(bitmap);
return 0;
}
GPU Fractal Implementation
• Assign the computation of each point to a processor
• Use a 2D block and the blockIdx.x and blockIdx.y
variables to determine which pixel we should be
working on
GPU Fractal
• __device__ makes this accessible from the compute
device
__device__ struct cuComplex {
float r;
float i;
__device__ cuComplex( float a, float b ) : r(a), i(b) {}
__device__ float magnitude2( void ) { return r * r + i * i; }
__device__ cuComplex operator*(const cuComplex& a) {
return cuComplex(r*a.r - i*a.i, i*a.r + r*a.i);
}
__device__ cuComplex operator+(const cuComplex& a) {
return cuComplex(r+a.r, i+a.i);
}
};
GPU Fractal
__device__ int julia(int x, int y)
{
// Same as CPU version
}
__global__ void kernel(char *ptr)
{
int x = blockIdx.x;
int y = blockIdx.y;
int offset = x + y * DIM;
ptr[offset] = julia(x,y);
}
GPU Fractal
int main()
{
FreeImage_Initialise();
FIBITMAP * bitmap = FreeImage_Allocate(DIM, DIM, 32);
char charmap[DIM][DIM];
char *dev_charmap;
cudaMalloc((void**)&dev_charmap, DIM*DIM*sizeof(char));
dim3 grid(DIM,DIM);
kernel<<<grid,1>>>(dev_charmap);
cudaMemcpy(charmap, dev_charmap, DIM*DIM*sizeof(char),
cudaMemcpyDeviceToHost);
GPU Fractal
RGBQUAD color;
for (int i = 0; i < DIM; i++){
for (int j = 0; j < DIM; j++){
color.rgbRed = 0;
color.rgbGreen = 0;
color.rgbBlue = 0;
if (charmap[i][j]!=0)
color.rgbBlue = 255;
FreeImage_SetPixelColor(bitmap, i, j, &color);
}
}
FreeImage_Save(FIF_BMP, bitmap, "output.bmp");
FreeImage_Unload(bitmap);
cudaFree(dev_charmap);
return 0;
}