CUDA: NVIDIA's C++ on the GPU

CS 463 Lecture, Dr. Lawlor

Here's a very simple CUDA program:
#include <iostream>
#include <cuda.h>

/* GPU code: set an array to a value */
__global__ void set_array(float *vals,float param) {
int i=threadIdx.x; /* find my index */

/* CPU code: memory movement and kernel calls */
int main(int argc,char *argv[]) {
int n=16; /* total number of floats */
float *vals; /* device array of n values */
cudaMalloc( (void**) &vals, n*sizeof(float) ); //Allocate GPU space

set_array<<<1,n>>>(vals,0.1234); /* Initialize the space on the GPU */

/* Copy a few elements back to CPU for printing */
int i=7;
float f=-999.0; /* CPU copy of value */
std::cout<<"vals["<<i<<"] = "<<f<<"\n";
return 0;

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Basically, we:
In the version below, we actually check errors, and we're allocating threads in 'blocks', which lets you use multiple blocks to work around the hardware's thread count limit of 512 or 1024 threads per block:
#include <iostream>
#include <cuda.h>
/* error checking */
#define check(cudacall) { int err=cudacall; if (err!=cudaSuccess) std::cout<<"CUDA ERROR "<<err<<" at line "<<__LINE__<<"'s "<<#cudacall<<"\n";}

/* GPU code: set an array to a value */
__global__ void set_array(float *vals,float param) {
int i=threadIdx.x+blockDim.x*blockIdx.x; /* find my index */
int main(int argc,char *argv[]) {
int w=4, h=4; /* number of blocks, threads per block */
int n=w*h; /* total number of floats */
float *vals; /* device array of n values */
check(cudaMalloc( (void**) &vals, n*sizeof(float) )); //Allocate some space

set_array<<<w,h>>>(vals,0.1234); /* Initialize the space on the GPU */

/* Copy a few elements back to CPU for printing */
for (int i=0;i<n;i+=3) {
float f=-999.0; /* CPU copy of value */
std::cout<<"vals["<<i<<"] = "<<f<<"\n";
return 0;

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Several interesting architectural details crop up in the CUDA documentation:

CUDA Concept
How big?
CPU Equivalent
One single execution of a kernel.  This is mostly conceptual, since the hardware operates on warps of threads. 1
function call
warp (of threads)
A group of 32 threads that all take the same branches.  The hardware does a good job with predication, so warps aren't "in your face" like with SSE instructions. 32 "threads"
SIMD unit ("floats")
block (of threads)
A group of a few hundred threads that have access to the same "__shared__" memory.  The block size is specified in software, and though limited by hardware to 512 or 1024 threads maximum, typically 256 threads per block performs best.  Serious slowdowns for non power of two, or less than 100 or so threads per block.  The PTX manual calls blocks "CTAs" (Cooperative Thread Arrays). 
About 256
One multicore thread
(Blocks run SMP/SMT style)
A set of blocks of threads, all running one __global__ function.
About a million threads
Parallel loop

Notice that despite what I think is *intentionally* obfuscated terminology, deep down there's a whole lot of similarity between GPU and CPU architectures; the GPU is just focused on wider problems.  The GPU's SIMD units are much wider, 32 floats at a time versus 8 at a time for AVX.  A typical GPU will have perhaps 8 multiprocessors (each called an SM or SMX), versus about 4 for a CPU.  The big difference is each multiprocessor is deeply hyperthreaded, able to run up to a hundred or so threads, typically limited by the physical register count, nowadays an absurd 65,536 registers per multiprocessor.  So you get really ridiculously good GPU performance if your code can fill all the threads, but if you've got single-threaded work, a GPU actually gives ridiculously bad performance!

The memory model is also highly segmented and specialized:

Speed (approx)
int i;
Local storage within a thread.
Several thousand per multiprocessor.
10TB/s (!!)
shared memory
__shared__ float arr[256];
Communication within a thread block.
16KB - 48KB/block
constant memory __const__ float *ptr Read-only prebroadcast memory, for kernel parameters and such. 64KB total, 8KB per multiprocessor. 1TB/s?
global memory
__global__ float *ptr
Communication across blocks, and with the CPU.
1GB or so.
texture memory
texture<float, 3> myTex;
2D and 3D read-only images, with caching and interpolation.
(same as global memory)
local memory
__local__ float arr[10];
Place for compiler to spill variables.  Much slower than registers--do not use.
(same as global memory)
host memory
cudaMemcpy( &device[0], &host[0], sizeof(float)*n, cudaMemcpyHostToDevice)
Only the CPU can do memory allocation, file I/O, network comms, etc.
2-4GB/s (PCIe)
Communicate across a cluster.
100MB/s (gigabit)

Note the large performance variation between top and bottom.  This means you can drastically improve performance by moving data closer to arithmetic.  For example, instead of storing most data on the host, leave everything in GPU global memory between kernel calls. 

CUDA Performance: Use Big Arrays!

One universal aspect of CUDA is that kernel calls (<<<), mallocs, and memcpy all take quite a long time to get running.  Once they're running, they're fairly fast, but for maximum efficiency you should plan on accessing about a megabyte each time!  Here's an example where I benchmark this length dependence:
#include <iostream>
#include <cuda.h>
#include "lib/inc.c"

float *dev_ptr=0, *host_ptr=0;
int len=0;

int time_memcpy(void) {
return 0;

__global__ void doGPUdatawrite(float *arr,float val) {
int i=blockIdx.x*blockDim.x+threadIdx.x;
int time_datawrite(void) {
int blocks=1, tpb=len;
while (tpb>=512) {blocks*=2; tpb/=2;}
return 0;

void time_sweep(const char *name,timeable_fn f) {
int max=1024*1024*4;
cudaMalloc((void **)&dev_ptr,max*sizeof(float));
cudaMallocHost((void **)&host_ptr,max*sizeof(float));
std::cout<<"Did mallocs\n";
for (len=64;len<=max;len*=4) {
double t=time_function(f);
printf("%s size %d: %.2f ns/float (%.1f us)\n",

int main(int argc,char *argv[]) {
std::cout<<"Starting up...\n";
time_sweep("GPU write",time_datawrite);
return 0;

(Try this in NetRun now!)

Here's the output on NetRun's NVIDIA GeForce GTX 280:
memcpy size 64: 163.73 ns/float (10.5 us)
memcpy size 256: 41.11 ns/float (10.5 us)
memcpy size 1024: 10.91 ns/float (11.2 us)
memcpy size 4096: 2.95 ns/float (12.1 us)
memcpy size 16384: 1.01 ns/float (16.6 us)
memcpy size 65536: 0.53 ns/float (34.7 us)
memcpy size 262144: 0.41 ns/float (108.3 us)
memcpy size 1048576: 0.38 ns/float (401.3 us)
memcpy size 4194304: 0.38 ns/float (1574.1 us)

GPU write size 64: 200.15 ns/float (12.8 us)
GPU write size 256: 50.37 ns/float (12.9 us)
GPU write size 1024: 12.65 ns/float (13.0 us)
GPU write size 4096: 3.21 ns/float (13.2 us)
GPU write size 16384: 0.85 ns/float (13.9 us)
GPU write size 65536: 0.25 ns/float (16.5 us)
GPU write size 262144: 0.10 ns/float (25.7 us)
GPU write size 1048576: 0.06 ns/float (62.8 us)
GPU write size 4194304: 0.05 ns/float (210.4 us)
Note that really big arrays, with millions of floats, take much less time per float than smaller arrays.  The basic trouble is that going through the OS, across the PCIe bus, into the GPU, and back takes like 10 microseconds (10us = 10000ns).   If you go to all that just to get one or two floats, you'll get terrible performance.

CUDA Application Performance: Mandelbrot Rendering

Here's a little Mandelbrot set rendering application in CUDA.  It includes benchmarking code, which shows some surprising results.
#include <cuda.h>
#include <iostream>
#include <fstream>
#include "lib/inc.c" /* NetRun utility functions, like time_in_seconds */

#define check(cudacall) { int err=cudacall; if (err!=cudaSuccess) std::cout<<"CUDA ERROR "<<err<<" at line "<<__LINE__<<"'s "<<#cudacall<<"\n";}

/* GPU Code! */
__global__ void fill_in_array(float *arr,int wid) {
int i=threadIdx.x+blockDim.x*blockIdx.x; // my thread's global number
int x=i%wid, y=i/wid; // my thread's pixel to work on

float cr=x*1.0/wid, ci=y*1.0/wid;
float zr=cr, zi=ci;
int count;
const int max_count=256;
for (count=0;count<max_count;count++) {
// z= z*z+c
float nzr=zr*zr-zi*zi + cr;
float nzi=2.0*zr*zi + ci;
if ((nzr*nzr+nzi*nzi)>4.0) break;
zr=nzr; zi=nzi;

/* Run on CPU */
int main(int argc,char *argv[]) {
int wid=512,ht=512;
float *arr=0; /* LIVES ON THE GPU!!!! */
double start=time_in_seconds(), elapsed;
check(cudaMalloc((void **)&arr, wid*ht*sizeof(float)));
std::cout<<"Allocated array: "<<elapsed*1.0e9/(wid*ht)<<"ns/pixel, time "<<elapsed<<" seconds\n";
start=time_in_seconds(); // <- reset the timer!
int threadsPerBlock=512;
int nBlocks=wid*ht/threadsPerBlock;

check(cudaThreadSynchronize()); // look for errors in kernel
std::cout<<"Rendered array: "<<elapsed*1.0e9/(wid*ht)<<"ns/pixel, time "<<elapsed<<" seconds\n";

float harr[wid*ht];
std::cout<<"Copied out array: "<<elapsed*1.0e9/(wid*ht)<<"ns/pixel, time "<<elapsed<<" seconds\n";

std::ofstream of("out.ppm",std::ios_base::binary);
of<<"P5\n"; // greyscale, binary
of<<wid<<" "<<ht<<"\n"; // image size
of<<"255\n"; // byte image
for (int i=0;i<wid*ht;i++) {
char c=(char)harr[i];
return 0;

(Try this in NetRun now!)

The surprising part about this is the timing breakdown:
Allocated array: 152.6ns/pixel, time 0.040 seconds
Rendered array: 3.4ns/pixel, time 0.001 seconds
Copied out array: 10.6ns/pixel, time 0.003 seconds
The biggest surprise is the cudaMalloc time, which is ridiculously huge because this is the first CUDA call in the program, so the driver has to locate and set up the graphics card.   Adding a "dummy" cudaMalloc of four bytes reduces the allocation time to less than a nanosecond per pixel.

Second, it takes 3x longer to copy the data off the card than it does to compute it!  Switching to a "char" data type cuts both the copy-out time (due to less data copied) as well as the rendering time (due to less data written)

(Try this in NetRun now!)

Allocated array: 0.02ns/pixel, time 5.11634e-06 seconds
Rendered array: 2.74ns/pixel, time 0.000719074 seconds
Copied out array: 4.63ns/pixel, time 0.00121307 seconds

CUDA for Cryptography

The easiest way to apply CUDA is to naturally parallel problems--things like:

It gets much harder if the threads need to communicate in any way; and many cryptographic operations with sequential dependencies, like the repetitive rounds of nearly any cipher, or the chained (dependent) blocks in CBC, can't be run on parallel hardware at all.

Generally, graphics hardware is oriented towards floating point work (they didn't have integer hardware until 2006 or so, well after programmability), yet I don't know of any popular ciphers that use any floating point.  For large-integer work, like most public key algorithms, you'd need a CUDA multiprecision arithmetic library; I've started looking at CUMP but I haven't found a modular arithmetic layer, and am beginning to doubt that one exists.