GPU AlgorithmsDavid Hauck

github.com/davidhauck

@david_hauck_mke

davidhauck40.blogspot.com

dhauck@skylinetechnologies.com

Graphics Processing Unit

Why?

Graphics Processing Unit

Graphics Processing Unit

General Purpose

T EM S

R

HOST

DEVICE

PCI Bus

Copy initial data to DEVICE

PCI Bus

Run DEVICE Executable

PCI Bus

Copy Results Back To HOST

Still Running on CPU

Still Running on CPUGPU is a Resource

MEMORYCONSCIOUSNESS

HOST DEVICEPOINTERSPOINTERS

int *a;

int *a;int *d_a;

arr = malloc(size);

arr = malloc(size);

cudaMalloc(&d_arr, size);

free(arr);

free(arr);

cudaFree(d_arr);

memcpy(dest, source, size);

memcpy(dest, source, size);

cudaMemcpy(&dest, src, size, …);

1: HOST DEVICE

2: EXECUTE

3: DEVICE HOST

1: HOST DEVICE

3: DEVICE HOST

cudaMemcpy();

1: HOST DEVICEcudaMemcpy(

&dest,source,size, ..hostToDevice);

EXECUTION

__global__ void myKernel(int *a){}

myKernel<<<1,1>>>(d_arr);

Let’s do an example

abcd

+

efgh

=

ijkl

abcd

+

efgh

=

ijkl

abcd

+

efgh

=

ijkl

threadIdx.x

0

1

2

3

int index = threadIdx.x;c[index] =

a[index] + b[index];

Let’s invent an ALGORITHM

K-Means Clustering

CODE

Shared Memory• ~48k• Multiple GB device memory (100x higher latency)• Access memory in order• 1 2 3• 4 5 6• 7 8 9

Considerations• Transistors are allocated to arithmetic, not memory. Sometimes

better to recompute rather than cache• Copying to/from host takes a while. Sometimes sequential operations

can stay on gpu• Avoid serialization (shared memory bank conflicts)• Asynchronous memory operations