I am trying to implement a general matrix-matrix multiplication OpenCL kernel, one that conforms to C = α*A*B + β*C.
The Kernel
I did some research online and decided to use a modified kernel from this website as a starting point. The main modification I have made is that allocation of local memory as working space is now dynamic. Below is the kernel I have written:
__kernel
void clkernel_gemm(const uint M, const uint N, const uint K, const float alpha,
__global const float* A, __global const float* B, const float beta,
__global float* C, __local float* Asub, __local float* Bsub) {
const uint row = get_local_id(0);
const uint col = get_local_id(1);
const uint TS = get_local_size(0); // Tile size
const uint globalRow = TS * get_group_id(0) + row; // Row ID of C (0..M)
const uint globalCol = TS * get_group_id(1) + col; // Row ID of C (0..N)
// Initialise the accumulation register
float acc = 0.0f;
// Loop over all tiles
const int numtiles = K / TS;
for (int t = 0; t < numtiles; t++) {
const int tiledRow = TS * t + row;
const int tiledCol = TS * t + col;
Asub[col * TS + row] = A[tiledCol * M + globalRow];
Bsub[col * TS + row] = B[globalCol * K + tiledRow];
barrier(CLK_LOCAL_MEM_FENCE);
for(int k = 0; k < TS; k++) {
acc += Asub[k * TS + row] * Bsub[col * TS + k] * alpha;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
C[globalCol * M + globalRow] = fma(beta, C[globalCol * M + globalRow], acc);
}
Tile Size (TS) is now a value defined in the calling code, which looks like this:
// A, B and C are 2D matrices, their cl::Buffers have already been set up
// and values appropriately set.
kernel.setArg(0, (cl_int)nrowA);
kernel.setArg(1, (cl_int)ncolB);
kernel.setArg(2, (cl_int)ncolA);
kernel.setArg(3, alpha);
kernel.setArg(4, A_buffer);
kernel.setArg(5, B_buffer);
kernel.setArg(6, beta);
kernel.setArg(7, C_buffer);
kernel.setArg(8, cl::Local(sizeof(float) * nrowA * ncolB));
kernel.setArg(9, cl::Local(sizeof(float) * nrowA * ncolB));
cl::NDRange global(nrowA, ncolB);
cl::NDRange local(nrowA, ncolB);
status = cmdq.enqueueNDRangeKernel(kernel, cl::NDRange(0), global, local);
The Problem
The problem I am encountering is, unit tests (written with Google's gtest) I have written will randomly fail, but only for this particular kernel. (I have 20 other kernels in the same .cl source file that pass tests 100% of the time)
I have a test that multiplies a 1x4 float matrix {0.0, 1.0, 2.0, 3.0} with a transposed version of itself {{0.0}, {1.0}, {2.0}, {3.0}}. The expected output is {14.0}.
However, I can get this correct result maybe just 75% of the time.
Sometimes, I can get 23.0 (GTX 970), 17.01 (GTX 750) or just -nan and 0.0 (all 3 devices). The curious part is, the respective incorrect results seem to be unique to the devices; I cannot seem to, for example, get 23.0 on the Intel CPU or the GTX 750.
I am baffled because if I have made an algorithmic or mathematical mistake, the mistake should be consistent; instead I am getting incorrect results only randomly.
What am I doing wrong here?
Things I have tried
I have verified that the data going into the kernels are correct.
I have tried to initialize both __local memory to 0.0, but this causes all results to become wrong (but frankly, I'm not really sure how to initialize it properly)
I have written a test program that only executes this kernel to rule out any race conditions interacting with the rest of my program, but the bug still happens.
Other points to note
I am using the C++ wrapper retrieved directly from the Github page.
To use the wrapper, I have defined CL_HPP_MINIMUM_OPENCL_VERSION 120 and CL_HPP_TARGET_OPENCL_VERSION 120.
I am compiling the kernels with the -cl-std=CL1.2 flag.
All cl::Buffers are created with only the CL_MEM_READ_WRITE flag.
I am testing this on Ubuntu 16.04, Ubuntu 14.04, and Debian 8.
I have tested this on Intel CPUs with the Intel OpenCL Runtime 16.1 for Ubuntu installed. The runtime reports that it supports up to OpenCL 1.2
I have tested this on both Nvidia GTX 760 and 970. Nvidia only supports up to OpenCL 1.2.
All 3 platforms exhibit the same problem with varying frequency.
This looks like a complicated one. There are several things to address and they won't fit into comments, so I'll post all this as an answer even though it does not solve your problem (yet).
I am baffled because if I have made an algorithmic or mathematical
mistake, the mistake should be consistent; instead I am getting
incorrect results only randomly.
Such a behavior is a typical indicator of race conditions.
I have tried to initialize both __local memory to 0.0, but this causes
all results to become wrong (but frankly, I'm not really sure how to
initialize it properly)
Actually this is a good thing. Finally we have some consistency.
Initializing local memory
Initializing local memory can be done using the work items, e.g. if you have a 1D workgroup of 16 items and your local memory consists of 16 floats, just do this:
local float* ptr = ... // your pointer to local memory
int idx = get_local_id(0); // get the index for the current work-item
ptr[idx] = 0.f; // init with value 0
barrier(CLK_LOCAL_MEM_FENCE); // synchronize local memory access within workgroup
If your local memory is larger, e.g. 64 floats, you will have to use a loop where each work item initializes 4 values, at least that is the most efficient way. However, no one will stop you from using every work item to initialize every value in the local memory, even though that is complete nonsense since you're essentially initializing it multiple times.
Your changes
The original algorithm looks like it is especially designed to use quadratic tiles.
__local float Asub[TS][TS];
__local float Bsub[TS][TS];
Not only that but the size of local memory matches the workgroup size, in their example 32x32.
When I look at your kernel parameters for local memory, I can see that you use parameters that are defined as M and N in the original algorithm. This doesn't seem correct.
Update 1
Since you have not described if the original algorithm works for you, this is what you should do to find your error:
Create a set of testdata. Make sure you only use data sizes that are actually supported by the original algorithm (e.g. minimum size, mulitples of x, etc.). Also, use large data sets since some errors only show if multiple workgroups are dispatched.
Use the original, unaltered algorithm with your testdata sets and verify the results.
Change the algorithm only that instead of fixed size local memory, dynamic local memory size is used, but make sure it has the same size as the fixed size approach. This is what you tried but I think it failed due to what I have described under "Your changes".
Related
I'm running experiments aiming to understand the behavior of random read and write access to global memory.
The following kernel reads from an input vector (groupColumn) with a coalesced access pattern and reads random entries from a hash table in global memory.
struct Entry {
uint group;
uint payload;
};
typedef struct Entry Entry;
__kernel void global_random_write_access(__global const uint* restrict groupColumn,
__global Entry* globalHashTable,
__const uint HASH_TABLE_SIZE,
__const uint HASH_TABLE_SIZE_BITS,
__const uint BATCH,
__const uint STRIDE) {
int global_id = get_global_id(0);
int local_id = get_local_id(0);
uint end = BATCH * STRIDE;
uint sum = 0;
for (int i = 0; i < end; i += STRIDE) {
uint idx = global_id + i;
// hash keys are pre-computed
uint hash_key = groupColumn[idx]; // coalesced read access
__global Entry* entry = &globalHashTable[hash_key]; // pointer arithmetic
sum += entry->payload; // random read
}
if (local_id < HASH_TABLE_SIZE) {
globalHashTable[local_id].payload = sum; // rare coalesced write
}
}
I ran this kernel on a NVIDIA V100 card with multiple iterations. The variance of the results is very low, thus, I only plot one dot per group configuration. The input data size is 1 GiB and each thread processes 128 entries (BATCH = 128). Here are the results:
So far so good. The V100 has a max memory bandwidth of roughly 840GiB/sec and the measurements are close enough, given the fact that there are random memory reads involved.
Now I'm testing random writes to global memory with the following kernel:
__kernel void global_random_write_access(__global const uint* restrict groupColumn,
__global Entry* globalHashTable,
__const uint HASH_TABLE_SIZE,
__const uint HASH_TABLE_SIZE_BITS,
__const uint BATCH,
__const uint STRIDE) {
int global_id = get_global_id(0);
int local_id = get_local_id(0);
uint end = BATCH * STRIDE;
uint sum = 0;
for (int i = 0; i < end; i += STRIDE) {
uint idx = global_id + i;
// hash keys are pre-computed
uint hash_key = groupColumn[idx]; // coalesced read access
__global Entry* entry = &globalHashTable[hash_key]; // pointer arithmetic
sum += i;
entry->payload = sum; // random write
}
if (local_id < HASH_TABLE_SIZE) {
globalHashTable[local_id].payload = sum; // rare coalesced write
}
}
Godbolt: OpenCL -> PTX
The performance drops significantly to a few GiB/sec for few groups.
I can't make any sense of the behavior. As soon as the hash table reaches the size of L1 the performance seems to be limited by L2. For fewer groups the performance is way lower. I don't really understand what the limiting factors are.
The CUDA documentation doesn't say much about how store instructions are handled internally. The only thing I could find is that the st.wb PTX instruction (Cache Operations) might cause a hit on stale L1 cache if another thread would try to read the same addess via ld.ca. However, there are no reads to the hash table involved here.
Any hints or links to understanding the performance behavior are much appreciated.
Edit:
I actually found a bug in my code that didn't pre-compute the hash keys. The access to global memory wasn't random, but actually coalesced due to how I generated the values. I further simplified my experiments by removing the hash table. Now I only have one integer input column and one interger output column. Again, I want to see how the writes to global memory actually behave for different memory ranges. Ultimately, I want to understand which hardware properties influence the performance of writes to global memory and see if I can predict based on the code what performance to expect.
I tested this with two kernels that do the following:
Read from input, write to output
Read from input, read from output and write to output
I also applied two different access patterns, by generating the values in the group column:
SEQUENTIAL: sequentially increasing numbers until current group's size is reached. This pattern leads to a coalesced memory access when reading and writing from the output column.
RANDOM: uni-distributed random numbers within the current group's size. This pattern leads to a misaligned memory access when reading and writing from the output column.
(1) Read & Write
__kernel void global_write_access(__global const uint* restrict groupColumn,
__global uint *restrict output,
__const uint BATCH,
__const uint STRIDE) {
int global_id = get_global_id(0);
int local_id = get_local_id(0);
uint end = BATCH * STRIDE;
uint sum = 0;
for (int i = 0; i < end; i += STRIDE) {
uint idx = global_id + i;
uint group = groupColumn[idx]; // coalesced read access
sum += i;
output[group] = sum; // write (coalesced | random)
}
}
PTX Code: https://godbolt.org/z/19nTdK
(2) Read, Read & Write
__kernel void global_read_write_access(__global const uint* restrict groupColumn,
__global uint *restrict output,
__const uint BATCH,
__const uint STRIDE) {
int global_id = get_global_id(0);
int local_id = get_local_id(0);
uint end = BATCH * STRIDE;
for (int i = 0; i < end; i += STRIDE) {
uint idx = global_id + i;
uint group = groupColumn[idx]; // coalesced read access
output[group] += 1; // read & write (coalesced | random)
}
}
PTX Code: https://godbolt.org/z/b647cz
As ProjectPhysX pointed out, the access pattern makes a huge difference. However, for small groups the performance is quite similar for both access patterns. In general, I would like to better understand the shape of the curves and which hardware properties, architectural features etc. influence this shape.
From the cuda programming guide I learned that global memory accesses are conducted via 32-, 64-, or 128-byte transactions. Accesses to L2 are done via 32-byte transactions. So up to 8 integer words can be accessed via a single transaction. This might explain the plateau with a bump at 8 groups at the beginning of the curve. After that more transactions are needed and performance drops.
One cache line is 128 bytes long (both on L1 and L2), hence, 32 intergers fit into a single cache line. For more groups more cache lines are required which can be potentially processed in parallel by more memory controllers. That might be the reason for the performance to increase here. 8 controllers are available on the V100 So I would expect the performance to peak at 256 groups. Though, it doesn't. Instead it will steadily increase performance until reaching 4096 groups and plateau there with roughly 750 GiB/sec.
The plateauing in your second performane plot is GPU saturation: For only a few work groups, the GPU is partly idle and the latencies involved in launching the kernel significantly reduce performance. Above 8192 groups, the GPU fully saturates its memory bandwidth. The plateau only is at ~520GB/s because of the misaligned writes (have low performance on the V100) and also the "rare coalesced write" in the if-block, which happens at least once per group. For branching within the group, all other threads have to wait for the single write operation to finish. Also this write is not coalesced, because it is not happening for every thread in the group. On the V100, misaligned write performance is very poor at max. ~120GB/s, see the benchmark here.
Note that if you would comment the if-part, the compiler sees that you do not do anything with sum and optimizes everything out, leaving you with a blank kernel in PTX.
The first performance graph to me is a bit more confusing. The only difference in the first kernel to the second is that the random wrtite in the loop is replaced by a random read. Generally, read performance on the V100 is much better (~840GB/s, regardless of coalesced/misaligned) than misaligned write performance, so performance is expected to be much better overall and indeed it is. However I can't make sense of the performance dropping for more groups, where saturation should theoretically be better. But the performance drop isn't really that significant at ~760GB/s vs. 730GB/s.
To summarize, you are observing that the performance penalty for misaligned writes (~120GB/s vs. ~900GB/s for coalesced writes) is much larger than for reads, where performance is about the same for coalesced/misaligned at ~840GB/s. This is common thing for GPUs, with some variance of course between microarchitectures. Typically there is at least some performance penalty for misaligned reads, but not as large as for misaligned writes.
I'm working with an 3D array of dimension xdim=49, ydim=1024 and zdim=64. my DEVICE_MAX_WORK_ITEM_SIZES is only 512/512/512. If I declare my
size_t global_work_size = {xdim, ydim, zdim}; and launch an 3D kernel,
I'm getting wrong results since my ydim > 512. If all my dimensions are below 512, I'm getting the expected results. Please let me know if there's an alternative for this?
CL_DEVICE_MAX_WORK_ITEM_SIZES only limits the size of work groups, not the global work item size (yea, it's a terrible name for the constant). You are much more tightly restricted by CL_DEVICE_MAX_WORK_GROUP_SIZE which is the total number of items allowed in a work group (you'd typically hit this far sooner than CL_DEVICE_MAX_WORK_ITEM_SIZES because of multiplication.
So go ahead an launch your global work size of 49, 1024, 64. It should work. If it's not, you're using get_local_id instead of get_global_id or have some other bug. We regularly launch 2D kernels with 4096 x 4096 global work size.
See also Questions about global and local work size
If you don't use shared local memory, you don't need to worry about local work group sizes. In fact, you can pass NULL instead of a pointer to an array of sizes for local_work_size and let the runtime pick something (it helps if your global dimensions are easily divisible by small numbers).
Assuming the dimensions you provided are the size of your data, you can decrease the global work size by making each GPU thread calculate more data. What I mean is, every thread in your case will do one calculation and if you change your kernels to do let's say 2 calculations in y dimension, than you could cut the number of threads you are firing into half. The global_work_size decides how many threads in each direction you are executing. Let me give you an example:
Let's assume you have an array you want to do some calculations with and the array size you have is 2048. If you write your kernel in the following way, you are going to need 2048 as the global_work_size:
__kernel void calc (__global int *A, __global int *B)
{
int i = get_global_id(0);
B[i] = A[i] * 5;
}
The global work size in this case will be:
size_t global_work_size = {2048, 1, 1};
However, if you change your kernel into the following kernel, you can lower your global work size as well: ()
__kernel void new_calc (__global int *A, __global int *B)
{
int i = get_global_id(0);
for (int ind = 0; ind < 8; ind++)
B[i*8 + ind] = A[i*8 + ind] * 5;
}
Then this way, you can use global size as:
size_t global_work_size = {256, 1, 1};
Also with the second kernel, each of your threads will execute more work, resulting in more utilisation.
I wrote the following code for my test NVIDIA and AMD GPUs
kernel void computeLayerOutput_Rolled(
global Layer* layers,
global float* weights,
global float* output,
constant int* restrict netSpec,
int layer)
{
const int n = get_global_size(0);
const int nodeNumber = get_global_id(0); //There will be an offset depending on the layer we are operating on
int numberOfWeights;
float t;
//getPosition(i, netSpec, &layer, &nodeNumber);
numberOfWeights = layers[layer].nodes[nodeNumber].numberOfWeights;
//if (sizeof(Layer) > 60000) // This is the extra code add for nvidia
// exit(0);
t = 0;
for (unsigned int j = 0; j != numberOfWeights; ++j)
t += threeD_access(weights, layer, nodeNumber, j, MAXSIZE, MAXSIZE) *
twoD_access(output, layer-1, j, MAXSIZE);
twoD_access(output, layer, nodeNumber, MAXSIZE) = sigmoid(t);
}
At the beginning, I did not add the code that checking the size of Layer, and it works on AMD Kalindi GPU, but crash and report an error code -36 on NVIDIA Tesla C2075.
Since I had rewritten the struct type Layer and decreased the size of it a lot before, I decided to check the size of Layer to determine whether this struct defined well in kernel code. Then I added this code
if (sizeof(Layer) > 60000)
exit(0);
Then it is OK on NVIDIA. However, the strange thing is, when I add // before this just as the given code above, it still works. (I believe I do not need to make clean && make when I rewrite something in kernel code, but I still did it) Nevertheless, when I roll back to the version not contains this comment, it fails and the error code -36 appears again. It really puzzles me. I think two versions of my code are identical, isn't it?
I'm using PyOpenCL to let my GPU do some regression on a large data set. Right now the GPU is slower than the CPU, probably because there is a loop that requires access to the global memory during each increment (I think...). The data set is too large to store into the local memory, but each loop does not require the entire data set, so I want to copy a portion of this array to the local memory. My question is: how do I do this? In Python one can easily slice a portion, but I don't think that's possible in OpenCL.
Here's the OpenCL code I'm using, if you spot any more potential optimisations, please shout:
__kernel void gpu_slope(__global double * data, __global double * time, __global int * win_results, const unsigned int N, const unsigned int Nmax, const double e, __global double * result) {
__local unsigned int n, length, leftlim, rightlim, i;
__local double sumx, sumy, x, y, xx, xy, invlen, a, b;
n = get_global_id(0);
leftlim = win_results[n*2];
rightlim = win_results[n*2+1];
sumx = 0;
sumy = 0;
xy = 0;
xx = 0;
length = rightlim - leftlim;
for(i = leftlim; i <= rightlim; i++) {
x = time[i]; /* I think this is fetched from global memory */
y = data[i];
sumx += x;
sumy += y;
xy += x*y;
xx += x*x;
}
invlen = 1.0/length;
a = xy-(sumx*sumy)*invlen;
b = xx-(sumx*sumx)*invlen;
result[n] = a/b;
}
I'm new to OpenCL, so please bear with me. Thanks!
The main(ish) point in GPU computing is trying to utilize hardware parallelism as much as possible. Instead of using the loop, launch a kernel with a different thread for every one of the coordinates. Then, either use atomic operations (the quick-to-code, but slow-performance option), or parallel reduction, for the various sums.
AMD has A tutorial on this subject. (NVidia does too, but theirs would be CUDA-based...)
You will find examples copying to local memory in PyOpenCL's examples folder: https://github.com/inducer/pyopencl/tree/master/examples
I recommend you read, run, and customize several of these examples to learn.
I also recommend the Udacity parallel programming course: https://www.udacity.com/course/cs344 This course will help solidify your grasp of fundamental OpenCL concepts.
I have the following opencl kernel function to get the column sum of a image.
__kernel void columnSum(__global float* src,__global float* dst,int srcCols,
int srcRows,int srcStep,int dstStep)
{
const int x = get_global_id(0);
srcStep >>= 2;
dstStep >>= 2;
if (x < srcCols)
{
int srcIdx = x ;
int dstIdx = x ;
float sum = 0;
for (int y = 0; y < srcRows; ++y)
{
sum += src[srcIdx];
dst[dstIdx] = sum;
srcIdx += srcStep;
dstIdx += dstStep;
}
}
}
I assign that each thread process a column here so that a lot of threads can get the column_sum of each column in parallel.
I also use float4 to rewrite the above kernel so that each thread can read 4 elements in a row at one time from the source image, which is shown below.
__kernel void columnSum(__global float* src,__global float* dst,int srcCols,
int srcRows,int srcStep,int dstStep)
{
const int x = get_global_id(0);
srcStep >>= 2;
dstStep >>= 2;
if (x < srcCols/4)
{
int srcIdx = x ;
int dstIdx = x ;
float4 sum = (float4)(0.0f, 0.0f, 0.0f, 0.0f);
for (int y = 0; y < srcRows; ++y)
{
float4 temp2;
temp2 = vload4(0, &src[4 * srcIdx]);
sum = sum + temp2;
vstore4(sum, 0, &dst[4 * dstIdx]);
srcIdx += (srcStep/4);
dstIdx += (dstStep/4);
}
}
}
In this case, theoretically, I think the time consumed by the second kernel to process a image should be 1/4 of the time consumed by the first kernel function. However, no matter how large the image is, the two kernels almost consume the same time. I don't know why. Can you guys give me some ideas? T
OpenCL vector data types like float4 were fitting better the older GPU architectures, especially AMD's GPUs. Modern GPUs don't have SIMD registers available for individual work-items, they are scalar in that respect. CL_DEVICE_PREFERRED_VECTOR_WIDTH_* equals 1 for OpenCL driver on NVIDIA Kepler GPU and Intel HD integrated graphics. So adding float4 vectors on modern GPU should require 4 operations. On the other hand, OpenCL driver on Intel Core CPU has CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT equal to 4, so these vectors could be added in a single step.
You are directly reading the values from "src" array (global memory). Which typically is 400 times slower than private memory. Your bottleneck is definitelly the memory access, not the "add" operation itself.
When you move from float to float4, the vector operation (add/multiply/...) is more efficient thanks to the ability of the GPU to operate with vectors. However, the read/write to global memory remains the same.
And since that is the main bottleneck, you will not see any speedup at all.
If you want to speed your algorithm, you should move to local memory. However you have to manually resolve the memory management, and the proper block size.
which architecture do you use?
Using float4 has higher instruction level parallelism (and then require 4 times less threads) so theoretically should be faster (see http://www.cs.berkeley.edu/~volkov/volkov10-GTC.pdf)
However did i understand correctly in you kernel you are doing prefix-sum (you store the partial sum at every iteration of y)? If so, because of the stores the bottleneck is at the memory writes.
I think on the GPU float4 is not a SIMD operation in OpenCL. In other words if you add two float4 values the sum is done in four steps rather than all at once. Floatn is really designed for the CPU. On the GPU floatn serves only as a convenient syntax, at least on Nvidia cards. Each thread on the GPU acts as if it is scalar processor without SIMD. But the threads in a warp are not independent like they are on the CPU. The right way to think of the GPGPU models is Single Instruction Multiple Threads (SIMT).
http://www.yosefk.com/blog/simd-simt-smt-parallelism-in-nvidia-gpus.html
Have you tried running your code on the CPU? I think the code with float4 should run quicker (potentially four times quicker) than the scalar code on the CPU. Also if you have a CPU with AVX then you should try float8. If the float4 code is faster on the CPU than float8 should be even faster on a CPU with AVX.
try to define __ attribute __ to kernel and see changes in run timing
for example try to define:
__ kernel void __ attribute__((vec_type_hint(int)))
or
__ kernel void __ attribute__((vec_type_hint(int4)))
or some floatN as you want
read more:
https://www.khronos.org/registry/cl/sdk/1.0/docs/man/xhtml/functionQualifiers.html