Numba cuda convolution

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Numba cuda convolution

There are three type of convolution filter in SDK. I mainly used convolutionTexture and convolutionSeparable application. Images used in final is provided by Andy see class website. I used 1kby1k, 2kby2k and 4kby4k image for performance testing. For some reason, 8kby8k image does not work well on my system. Intermediate and Final version of application is available to download and test.

See more details in section 7 below. Since there is well optimized application is available in SDK, I started to look at their code. First of all, original code uses random data instead of real images.

Each data pixel in image represented as single float in these application. To make this application work with real image, I implemeted image loader and writer in RAW format. This code was slightly modified the module we used in project 2.

Each color component of pixel is composed of three values, RGB. To apply convolution filter on image, there are two ways. The first one is simply to map each component as single float and run convolution filter three times for each channel.

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The second apporach is to modify the original code to use uchar4 or int type as dataset so that we can compute separate channel value within CUDA kernel.

I implemeted both ways in convolutionTexuture and convolutionSeparable but later on I only used the first method since it makes kernel code much simpler. First thing I tried is top-down approach. I mean I took nvidia application and started to remove some of optimization techniques. Then, realized that it is not easy to make this all the way down to naive approach. So, restarted implementation from the most naive way and optimized it to close to the nvidia's application.

Later section will explain these steps. If you are not familiar with convolution filter, please take a look wikipedia entry, Gaussian blur or Convolution. Also, class lecture note week4, convolution is useful.

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From the idea of convolutio filter itself, the most naive approach is to use global memory to send data to device and each thread accesses this to compute convolution kernel. Our convolution kernel size is radius 8 total 17x17 multiplicaiton for single pixel value. In image border area, reference value will be set to 0 during computation.

numba cuda convolution

This naive approach includes many of conditional statements and this causes very slow execution. There is no idle threads since total number of threads invoked is the same as total pixel numbers. CUDA kernel block size is 16x Below execution time is a mean value over 10 times execution. As you can see, it is extremely slow here.

Figure 1. Memory Access Pattern in Naive approach: each threads in block access 17x17 times global memory. We all experienced the importance of shared memory throughout project 2. Now, it is time to incorporate with this feature from naive code. When I read nvidia convolution document, I thought that it is OK to invoke many of threads and each thread load data from global mem to shared mem. Then, let some of threads idle. Those are thread loaded apron pixels and do not compute convolution.The convolution operator is often seen in signal processing, where it models the effect of a linear time-invariant system on a signal [1].

In probability theory, the sum of two independent random variables is distributed according to the convolution of their individual distributions.

If v is longer than athe arrays are swapped before computation. At the end-points of the convolution, the signals do not overlap completely, and boundary effects may be seen. Boundary effects are still visible. The convolution product is only given for points where the signals overlap completely.

Values outside the signal boundary have no effect. Discrete, linear convolution of a and v. Since multiplication is more efficient faster than convolution, the function scipy. Only return the middle values of the convolution.

Contains boundary effects, where zeros are taken into account:. The two arrays are of the same length, so there is only one position where they completely overlap:. Returns: out : ndarray Discrete, linear convolution of a and v. See also scipy. Same output as convolve, but also accepts poly1d objects as input. Previous topic numpy. Last updated on Jul 26, Created using Sphinx 1.Donald Knuth famously quipped that "premature optimization is the root of all evil.

CUDA Convolution

In the Python world, there is another cost to optimization: optimized code often is written in a compiled language like Fortran or C, and this leads to barriers to its development, use, and deployment. Too often, tutorials about optimizing Python use trivial or toy examples which may not map well to the real world.

I've certainly been guilty of this myself. Here, I'm going to take a different route: in this post I will outline the process of understanding, implementing, and optimizing a non-trivial algorithm in Python, in this case the Non-uniform Fast Fourier Transform NUFFT. Along the way, we'll dig into the process of optimizing Python code, and see how a relatively straightforward pure Python implementation, with a little help from Numbacan be made to nearly match the performance of a highly-optimized Fortran implementation of the same algorithm.

First, I want to answer the inevitable question: why spend the time to make a Python implementation of an algorithm that's already out there in Fortran? The reason is that I've found in my research and teaching that pure-Python implementations of algorithms are far more valuable than C or Fortran implementations, even if they might be a bit slower. This is for a number of reasons:. Pure-Python code is easier to read, understand, and contribute to. Good Python implementations are much higher-level than C or Fortran, and abstract-away loop indices, bit twiddling, workspace arrays, and other sources of code clutter.

A typical student reading good Python code can immediately understand and modify the algorithm, while the same student would be lost trying to understand typical optimized Fortran code.

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Pure-python packages are much easier to install than Python-wrapped C or Fortran code. This is especially true on non-Linux systems. Fortran in particular can require some installation prerequisites that are non-trivial for many users. In practice, I've seen people give up on better tools when there is an installation barrier for those tools. Pure-python code often works for many data types. Because of the way it is written, pure Python code is often automatically applicable to single or double precision, and perhaps even to extensions to complex numbers.

For compiled packages, supporting and compiling for all possible types can be a burden.

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Pure-python is easier to use at scale. Certainly code speed will overcome these considerations if the performance gap is great enough, but I've found that for many applications a pure Python package, cleverly designed and optimized, can be made fast enough that these larger considerations win-out. The challenge is making the Python fast. We'll explore this below. But what about when your grid is not uniform? The NUFFT is an algorithm which converts the non-uniform transform into an approximate uniform transform, not with error-prone interpolation, but instead using a clever "gridding" operation motivated by the convolution theorem.

When developing optimized code, it is important to start with something easy to make sure you're on the right track. Here we'll start with a straightforward direct version of the non-uniform Fourier transform. The arguments for the latter include iflagwhich is a positive or negative number indicating the desired sign of the exponent:. Again, I can't emphasize this enough: when writing fast code, start with a slow-and-simple version of the code which you know gives the correct result, and then optimize from there.

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The results match! A quick check shows that, as we might expect, the Fortran algorithm is orders of magnitude faster:. Let's see if we can do better. For later convenience, we'll start by defining a utility to compute the grid parameters as detailed in the NUFFT paper. Let's compare this to the previous results.This article shows the fundamentals of using CUDA for accelerating convolution operations.

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Since convolution is the important ingredient of many applications such as convolutional neural networks and image processing, I hope this article on CUDA would help you to know about convolution and its parallel implementation. I believe in that writing software is a powerful way to understand and demonstrate what one would like to realize. In this version, only global memory of a GPU is used. A convolution operation for the image can be represented by the following equation:. That is, a multiply-add operation is performed in the neighborhood.

Figure 1. A coordinate system of an image and a filter. Figure 2. An example of a convolution operation. Note that there is no causality caused by time among pixel data in an image, the order of the multiply operations can be arbitrary as long as the correspondences between pixel data and filter coefficients are correctly kept.

For example, the convolution operation can be represented by the following equation as well:. The filtering result is obtained by performing the convolution operation for every pixel in the image, i. Since corresponding pixels cannot be found out for some filter coefficients at the edges of the image as shown in Fig.

Appending pixels is called padding and Figure 4 shows typical examples of it, zero padding and replication padding. Figure 3. The convolution operations at the edges of the image.

Figure 4. Examples of padding; zero padding and replication padding. The convolution operation of Eq. Thus parallel computation is very useful to shorten the computation time of filtering. Figure 5 shows the overview of the architecture of a GPU. Parallel computing is performed by assigning a large number of threads to CUDA cores. Figure 5. The overview of the architecture of a GPU. Figure 6.

The information obtained by the command is important to write a program using CUDA. In software implementations, the convolution operations for filtering can be realized by a nested loop structure. Allocated one-dimensional 1D arrays are used for the images and the filter, because using 1D arrays is convenient to allocate a memory space on a GPU. As we can see from this code, the convolution operations can be parallelized because the operation for a pixel coordinate is independent from the others.

In order to parallelize the convolution operations using CUDA, an image is divided into blocks by a grid as shown in Fig.

Therefore, what we have to do is writing the program for the threads to perform the convolution operations. Figure 7. The hierarchy of data defined by a grid. A computer and a GPU are called a host and a device respectively, and have their own memory. An image and a filter are loaded to host memory by host code and they have to be transferred to device memory to carry out parallel processing. In this article, a type of device memory called global memory is used.

Global memory is also referred to as graphics memory and the total amount of it can be checked by the command deviceQuery as shown in Fig. The function cudaMalloc is used to allocate a memory space on a device, while new is used for a host. Note that since transferring data between a host and a device may not be so fast compared to computation itself due to the bandwidth limitation of a bus between a CPU and a GPU, you have to consider the effectiveness of introducing GPU including the elapsed time for data transfer.The cuda.

If a signature is supplied, then a function is returned that takes a function to compile. A function to JIT compile, or a signature of a function to compile. CUDA Kernel object. When called, the kernel object will specialize itself for the given arguments if no suitable specialized version already exists and launch on the device associated with the current context.

Kernel objects are not to be constructed by the user, but instead are created using the numba. Return the generated assembly code for all signatures encountered thus far, or the LLVM IR for a specific signature if given. Produce a dump of the Python source of this function annotated with the corresponding Numba IR and type information. The dump is written to fileor sys.

Compile and bind to the current context a version of this kernel specialized for the given args. Individual specialized kernels are instances of numba. CUDAKernel :. CUDA Kernel specialized for a given set of argument types.

Better Python Parallelization with Numba on CPU and GPU

When called, this object will validate that the argument types match those for which it is specialized, and then launch the kernel on the device. The remainder of the attributes and functions in this section may only be called from within a CUDA Kernel. The thread indices in the current thread block, accessed through the attributes xyand z.

Each index is an integer spanning the range from 0 inclusive to the corresponding value of the attribute in numba. The block indices in the grid of thread blocks, accessed through the attributes xyand z.

The shape of a block of threads, as declared when instantiating the kernel. This value is the same for all threads in a given kernel, even if they belong to different blocks i. The shape of the grid of blocks, accressed through the attributes xyand z. Return the absolute position of the current thread in the entire grid of blocks.

If ndim is 1, a single integer is returned. If ndim is 2 or 3, a tuple of the given number of integers is returned. Return the absolute size or shape in threads of the entire grid of blocks. Creates an array in the local memory space of the CUDA kernel with the given shape and dtype.

Copies the ary into constant memory space on the CUDA kernel at compile time. Returns an array like the ary argument. Support int32, int64, float32 and float64 only. The idx argument can be an integer or a tuple of integer indices for indexing into multiple dimensional arrays. The number of element in idx must match the number of dimension of array. Returns the value of array[idx] before the storing the new value. Behaves like an atomic load.

High-Performance Computing with Python: Numba and GPUs

Synchronize all threads in the same thread block. This function implements the same pattern as barriers in traditional multi-threaded programming: this function waits until all threads in the block call it, at which point it returns control to all its callers.In CUDA, the code you write will be executed by multiple threads at once often hundreds or thousands.

Your solution will be modeled by defining a thread hierarchy of gridblocks and threads. For all but the simplest algorithms, it is important that you carefully consider how to use and access memory in order to minimize bandwidth requirements and contention.

It gives it two fundamental characteristics:. It might seem curious to have a two-level hierarchy when declaring the number of threads needed by a kernel. The block size i. To help deal with multi-dimensional arrays, CUDA allows you to specify multi-dimensional blocks and grids. In the example above, you could make blockspergrid and threadsperblock tuples of one, two or three integers.

It therefore has to know which thread it is in, in order to know which array element s it is responsible for complex algorithms may define more complex responsibilities, but the underlying principle is the same. One way is for the thread to determine its position in the grid and block and manually compute the corresponding array position:.

Unless you are sure the block size and grid size is a divisor of your array size, you must check boundaries as shown above. These objects can be 1D, 2D or 3D, depending on how the kernel was invoked. To access the value at each dimension, use the xy and z attributes of these objects, respectively. The thread indices in the current thread block. For 1D blocks, the index given by the x attribute is an integer spanning the range from 0 inclusive to numba.

A similar rule exists for each dimension when more than one dimension is used. The shape of the block of threads, as declared when instantiating the kernel.

numba cuda convolution

This value is the same for all threads in a given kernel, even if they belong to different blocks i. The block indices in the grid of threads launched a kernel.

For a 1D grid, the index given by the x attribute is an integer spanning the range from 0 inclusive to numba. The shape of the grid of blocks, i. Simple algorithms will tend to always use thread indices in the same way as shown in the example above.

Numba provides additional facilities to automate such calculations:. Return the absolute position of the current thread in the entire grid of blocks.I wanted to write a post comparing various multiprocessing strategies, but without relying on a trivial example. I recently came across this post on stackoverflow. I deal with geo-data in my day job quite often so felt that I could present a reasonable answer to this challenge:.

CUDA Convolution

Given two arrays of coordinates coord1, coord2count the number of coordinates in coord2 within one kilometer of the coordinates in coord1. The end result should be a 1-dimensional array of the same length as coord1. The full data set is on the order of 10 million and 1 million coordinates.

This means that if we were to compare the distances between all pair-wise combinations, we would need to calculate 10 trillion distances. This meant that I was going to need to leverage every trick I knew to finish this task in a reasonable time frame. This came from a brilliant suggestion in the comments. As it turns out, the absolute distance between any two latitudes is relatively constant on the surface of the earth.

So a simple latitude bounding box can spare many distance calculations the same cannot be said of longitudes.

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Since distance will be calculated numerous times, I wanted this to be as swift as possible. Given my fondness for Numba, I quickly ported this code from this stackoverflow post. Comparing the three in terms of distances calculated per hour:. Thanks Numba for the 40x speed up! Fortunately, for this case, Numba is the simplest as is demonstrated in the follow coding pattern:. Frankly, I find this much easier then utilizing the multiprocessing library.

numba cuda convolution

In multiprocessingwe need to manage memory, global variables all get copied troublesome for large amounts of data to each process, and we need to worry about syncing processes. The notebook does include an example using starmapbut Numba outperforms it by a large margin. The class also lets you keep the three lesson notebooks for future reference. I found it to be an excellent intro and used the knowledge there to write a CUDA solution for this problem.

The cuda. This allows subsequent kernels to invoke this method. In terms of performance:. But in terms of raw speed, the GPU can calculate distances much faster. This lecture does a pretty good job of explaining these details as well as the DLI lesson linked above.

Essentially, the GPU is divided into multiple configurable components where a grid represents a collection of blocks, a block represents a collection of threads, and each thread is capable of behaving as a processor. However, if I want to be able to iterate over the entire data set, I need to use the convenience functions. Otherwise, a for-loop would only iterate within a block whose size is much smaller.


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