About The Class Motivation GPU Architecture C for CUDA Sample Code oooooooo oooooooo ooooooooo ooooo ooooooooooo Introduction, CUDA Basics in Fihpovic Fall 2017 Jiří Filipovič Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code •ooooooo oooooooo ooooooooo ooooo ooooooooooo Language I will speak English if there is any foreign student in the class • expect Slovak :-) But I understand that • my English is not perfect <* your English may not be perfect o so feel free to interrupt me and ask me, if you do not understand If you do not feel comfortable to ask me in English • ask me in Czech/Slovak Jiří Filipovič Introduction, CUDA Basics About The Class O0OOOOOO Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo About the class The class is focused on algorithm design and programming of general purpose computing applications on many-core vector processors Jiří Filipovič Introduction, CUDA Basics About The Class O0OOOOOO Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo About the class The class is focused on algorithm design and programming of general purpose computing applications on many-core vector processors We will focus to CUDA GPUs first: • C for CUDA is good for teaching (easy API, a lot of examples available, mature compilers and tools) • restricted to NVIDIA GPUs and x86 CPUs (with PGI) Jiří Filipovič Introduction, CUDA Basics About The Class O0OOOOOO Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo About the class The class is focused on algorithm design and programming of general purpose computing applications on many-core vector processors We will focus to CUDA GPUs first: • C for CUDA is good for teaching (easy API, a lot of examples available, mature compilers and tools) • restricted to NVIDIA GPUs and x86 CPUs (with PGI) After learning CUDA, we focus to OpenCL • programming model very similar to CUDA, easy to learn when you already know CUDA • can be used with various HW devices o we will focus on code optimizations for x86, Intel MIC (Xeon Phi) and AMD GPUs The class is practically oriented - besides efficient parallelization, we will focus on writing efficient code. J in Fihpovic Introduction, CUDA Basics About The Class OO0OOOOO Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo What is offered You will learn: • architecture of NVIDIA and AMD GPUs, Xeon Phi • architecture-aware design of data-parallel algorithms 9 programming in C for CUDA and OpenCL • performance tuning and profiling • basic tools and libraries for CUDA GPUs <* use cases Jiří Filipovič Introduction, CUDA Basics About The Class OOO0OOOO Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo What is expected from you During the semester, you will work on a practically oriented project o important part of your total score in the class • the same task for everybody, we will compare speed of your implementation • 50 + 20 points of total score • working code: 25 points a efficient implementation: 25 points • speed of your code relative to your class mates: at most 20 points (only to improve your final grading) Exam (oral or written, depending on the number of students) 50 points Jiří Filipovič Introduction, CUDA Basics About The Class OOOO0OOO Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Grading For those finishing by exam: o A: 92-100 9 B: 86-91 9 C: 78-85 9 D: 72-77 9 E: 66-71 9 F: 0-65 pts For those finishing by colloquium: • 50 pts Jiří Filipovič Introduction, CUDA Basics About The Class ooooo«oo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Materials - CUDA CUDA documentation (installed as a part of CUDA Toolkit, downloadable from developer.nvidia.com) 9 CUDA C Programming Guide (most important properties of CUDA) • CUDA C Best Practices Guide (more detailed document focusing on optimizations) • CUDA Reference Manual (complete description of C for CUDA API) 9 other useful documents (nvcc guide, PTX language description, library manuals, .. .) CUDA article series, Supercomputing for the Masses • http://www.ddj.com/cpp/207200659 J in Fihpovic Introduction, CUDA Basics About The Class OOOOOO0O Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Materials - OpenCL • OpenCL 1.1 Specification • AMD Accelerated Parallel Processing Programming Guide 9 Intel OpenCL SDK Programming Guide • Writing Optimal OpenCL Code with Intel OpenCL SDK Jin Fihpovic □ i3" Introduction, CUDA Basics About The Class ooooooo* Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Materials - Parallel Programming a Ben-Ari M., Principles of Concurrent and Distributed Programming, 2nd Ed. Addison-Wesley, 2006 9 Timothy G. Mattson, Beverly A. Sanders, Berna L. Massingill, Patterns for Parallel Programming, Addison-Wesley, 2004 Jiří Filipovič Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code OOOOOOOO «0000000 ooooooooo ooooo ooooooooooo Motivation - GPU arithmetic performance Theoretical GFLOP/s 5750 5500 Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08 Jul-09 Nov-10 Apr-12 Aug-13 Dec-14 J in Fihpovic Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code oooooooo o«oooooo ooooooooo ooooo ooooooooooo Motivation - GPU memory bandwidth Theoretical GB/s 360 330 300 270 240 210 180 150 120 90 60 GeForce FX 5900 GeForce 780 Ti Ť Tesla GeForce GPU Tesla GPU- Tesla K20X GeForce GTX4S0 GeForce GTX GeForcq 6S00 GT 30 GeForce SSO0 GTX GeForce 7800 GTX North wood 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 J in Fihpovic Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code OOOOOOOO OO0OOOOO ooooooooo ooooo ooooooooooo Motivation - programming complexity OK, GPUs are more powerful, but GPU programming is substantially more difficult, right? 9 well, it is more difficult comparing to writing serial C/C++ code... 9 but can we compare it to serial code? Jiří Filipovič Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code OOOOOOOO OO0OOOOO ooooooooo ooooo ooooooooooo Motivation - programming complexity OK, GPUs are more powerful, but GPU programming is substantially more difficult, right? 9 well, it is more difficult comparing to writing serial C/C++ code... 9 but can we compare it to serial code? Moore's Law Number of transistors on a single chip doubles every 18 months J Jiří Filipovič Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code OOOOOOOO OO0OOOOO ooooooooo ooooo ooooooooooo Motivation - programming complexity OK, GPUs are more powerful, but GPU programming is substantially more difficult, right? 9 well, it is more difficult comparing to writing serial C/C++ code... 9 but can we compare it to serial code? Moore's Law Number of transistors on a single chip doubles every 18 months Corresponding growth of performance comes from • in the past: frequency increase, instruction parallelism, out-of-order instruction processing, caches, etc. • today: vector instructions, increase in number of cores J in Fihpovic Introduction, CUDA Basics About The Class oooooooo Motivation OOO0OOOO GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Motivation - paradigm change Moore's Law consequences: • in the past:changes were important for compiler developers; application developers didn't need to worry o today: in order to utilize state-of-the-art processors, it is necessary to write parallel and vectorized code • it is necessary to find parallelism in the problem being solved, which is a task for a programmer, not for a compiler (at least for now) • writing efficient code for modern CPUs is similarly difficult as writing for GPUs Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooo«ooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Electrostatic Potential Map Important problem from computational chemistry 9 we have a molecule defined by position and charges of its atoms • the goal is to compute charges at a 3D spatial grid around the molecule In a given point of the grid, we have Wj J u Where wj is charge of the j-th atom, r,y is Euclidean distance between atom j and the grid point / and eo is vacuum permittivity. J in Fihpovic Introduction, CUDA Basics About The Class oooooooo Motivation OOOOO0OO GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Electrostatic Potential Map Initial implementation • suppose we know nothing about HW, just know C++ • algorithm needs to process 3D grid such that it sums potential of all atoms for each grid point • we will iterate over atoms in outer loop, as it allows to precompute positions of grid points and minimizes number of accesses into input/output array Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation OOOOOO0O GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Electrostatic Potential Map void coulomb(const sAtom* atoms, const int nAtoms, const float gs , const int gSize , float *grid) { for (int a = 0; a < nAtoms; a++) { sAtom myAtom = atoms[a]; for (int x = 0; x < gSize; x++) { float dx2 = powf((float)x * gs — myAtom.x, 2.Of) for (int y = 0; y < gSize; y++) { float dy2 = powf((float)y * gs — myAtom.y); for (int z = 0; z < gSize; z++) { float dz = (float)z * gs — myAtom.z; float e = myAtom.w / sqrtf(dx2 + dy2 + dz*dz grid[z*gSize*gSize + y*gSize + x] += e; } } } } } J in Fihpovic Introduction, CUDA Basics About The Class oooooooo Motivation ooooooo* GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Electrostatic Potential Map Execution on 4-core CPU at 3.6GHz (Sandy Bridge) + GeForce GTX 1070 (Pascal) • naive implementation 164.7 millions of atoms evaluated per second (MEvals/s) Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation ooooooo* GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Electrostatic Potential Map Execution on 4-core CPU at 3.6GHz (Sandy Bridge) + GeForce GTX 1070 (Pascal) • naive implementation 164.7 millions of atoms evaluated per second (MEvals/s) • 476.9 Mevals/s when optimized cache: 2.9x speedup Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation ooooooo* GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Electrostatic Potential Map Execution on 4-core CPU at 3.6GHz (Sandy Bridge) + GeForce GTX 1070 (Pascal) • naive implementation 164.7 millions of atoms evaluated per second (MEvals/s) • 476.9 Mevals/s when optimized cache: 2.9x speedup • 2,577 Mevals/s when vectorized: 15.6x speedup Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation ooooooo* GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Electrostatic Potential Map Execution on 4-core CPU at 3.6GHz (Sandy Bridge) + GeForce GTX 1070 (Pascal) • naive implementation 164.7 millions of atoms evaluated per second (MEvals/s) • 476.9 Mevals/s when optimized cache: 2.9x speedup • 2,577 Mevals/s when vectorized: 15.6x speedup • 9,914 Mevals/s when parallelized: 60.2x speedup Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation ooooooo* GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooooo Electrostatic Potential Map Execution on 4-core CPU at 3.6GHz (Sandy Bridge) + GeForce GTX 1070 (Pascal) • naive implementation 164.7 millions of atoms evaluated per second (MEvals/s) • 476.9 Mevals/s when optimized cache: 2.9x speedup • 2,577 Mevals/s when vectorized: 15.6x speedup • 9,914 Mevals/s when parallelized: 60.2x speedup • 537,900 Mevals/s GPU version: 3266x speedup GPU speedup over already tuned CPU code is 54x. However, a lot of existing CPU code is not tuned at all. In this class, you will learn how to tune the code. Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation GPU Architecture OOOOOOOO «00000000 C for CUDA ooooo Sample Code ooooooooooo Why are GPUs so powerful? Types of Parallelism • Task parallelism • decomposition of a task into the problems that may be processed in parallel • usually more complex tasks performing different actions • usually more frequent (and complex) synchronization • ideal for small number of high-performance processors • Data parallelism • parallelism on the level of data structures 9 usually the same operations on many items of a data structure • finer-grained parallelism allows for simple construction of individual processors Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation GPU Architecture oooooooo o«ooooooo C for CUDA ooooo Sample Code ooooooooooo Why are GPUs so powerful? From programmer's perspective • some problems are rather data-parallel, some task-parallel (graph traversal vs. edge processing) From hardware perspective 9 processors for data-parallel tasks may be simpler 9 it is possible to achieve higher arithmetic performance with the same size of processor • simpler memory access patterns allow for high-throughput memory designs Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture OO0OOOOOO C for CUDA ooooo Sample Code ooooooooooo GPU Architecture Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture OOO0OOOOO C for CUDA ooooo Sample Code ooooooooooo GPU Architecture Main differences compared to CPU high parallelism: tens thousands threads needed to utilize high-end GPUs • SIMT model: subsets of threads runs in lock-step mode • distributed on-chip memory: subsets of threads shares their private memory o restricted caching capabilities: small cache, often read-only Algorithms usually need to be redesigned to be efficient on GPU. Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture oooo«oooo C for CUDA ooooo Sample Code ooooooooooo GPU Architecture Within the system: • co-processor with dedicated memory (discrete GPU) • asynchronous processing of instructions • attached using PCI-E to the rest of the system (discrete GPU) Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo CUDA Motivation oooooooo GPU Architecture OOOOO0OOO C for CUDA ooooo Sample Code ooooooooooo CUDA (Compute Unified Device Architecture) • architecture for parallel computations developed by NVIDIA provides a new programming model, allows efficient implementation of general GPU computations • may be used in multiple programming languages Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture OOOOOO0OO C for CUDA ooooo Sample Code ooooooooooo G80 Processor 9 first CUDA processor • 16 multiprocessors • each multiprocessor 8 scalar processors • 2 units for special functions • up to 768 threads • HW for thread switching and scheduling • threads are grouped into warps by 32 • SIMT • native synchronization within the multiprocessor J in Fihpovic □ [31 Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture OOOOOOO0O C for CUDA ooooo Sample Code ooooooooooo G80 Memory Model Memory model • 8192 registers shared among all threads of a multiprocessor • 16 kB of shared memory • local within the multiprocessor • as fast as registry (under certain constraints) • constant memory • cached, read-only • texture memory • cached with 2D locality, read-only • global memory o non cached, read-write • data transfers between global memory and system memory through PCI-E j in Fihpovic Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture oooooooo« C for CUDA ooooo Sample Code ooooooooooo G80 Processor Device Multiprocessor N Multiprocessor 2 Multiprocessor 1 Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA •oooo Sample Code ooooooooooo C for CUDA C for CUDA is extension of C for parallel computations 9 explicit separation of host (CPU) and device (GPU) code • thread hierarchy • memory hierarchy • synchronization mechanisms • API S1 Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA O0OOO Sample Code ooooooooooo Thread Hierarchy Thread hierarchy • threads are organized into blocks 9 blocks form a grid • problem is decomposed into sub-problems that can be run independently in parallel (blocks) • individual sub-problems are divided into small pieces that can be run cooperatively in parallel (threads) • scales well Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA oo«oo Sample Code ooooooooooo Thread Hierarchy Grid Block (0, 0) Block (1, 0) Block (2, 0) Block (1,1) Thread (0, 0) Thread (1, 0) Thread (2, 0) Thread (3, 0) Thread (0,1) Thread (1,1) Thread (2,1) Thread (3,1) Thread (0, 2) Thread (1, 2) Thread (2, 2) Thread (3, 2) Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooo«o Sample Code ooooooooooo Memory Hierarchy More memory types: • different visibility • different lifetime 9 different speed and behavior • brings good scalability Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA oooo» Sample Code ooooooooooo Memory Hierarchy Thread Per-thread local memory Thread Block Per-block shared memory GridO Block (0, 0) Block (1, 0) Block (2, 0) Block (0,1) Block (1,1) Block (2,1) Gridl Block (0,0) Block (1,0) Block (0,1) Block (1,1) Block (0, 2) Block (1, 2) Global memory J in Fihpovic Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code •oooooooooo An Example - Sum of Vectors We want to sum vectors a and b and store the result in vector c Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code •oooooooooo An Example - Sum of Vectors We want to sum vectors a and b and store the result in vector c We need to find parallelism in the problem. Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code •oooooooooo An Example - Sum of Vectors We want to sum vectors a and b and store the result in vector c We need to find parallelism in the problem. Serial sum of vectors: for (int i = 0; i < N; i++) c[i] = a[i] + b[i]; Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code •oooooooooo An Example - Sum of Vectors We want to sum vectors a and b and store the result in vector c We need to find parallelism in the problem. Serial sum of vectors: for (int i = 0; i < N; i++) c[i] = a[i] + b[i]; Individual iterations are independent - it is possible to parallelize, scales with the size of the vector. Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code •oooooooooo An Example - Sum of Vectors We want to sum vectors a and b and store the result in vector c We need to find parallelism in the problem. Serial sum of vectors: for (int i = 0; i < N; i++) c[i] = a[i] + b[i]; Individual iterations are independent - it is possible to parallelize, scales with the size of the vector. i-th thread sums i-th component of the vector: c[i] = a[i] + b[i]; How do we find which thread we are? Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code O0OOOOOOOOO Thread Hierarchy Grid Block (0, 0) Block (1, 0) Block (2, 0) Block (1,1) Thread (0, 0) Thread (1, 0) Thread (2, 0) Thread (3, 0) Thread (0,1) Thread (1,1) Thread (2,1) Thread (3,1) Thread (0, 2) Thread (1, 2) Thread (2, 2) Thread (3, 2) Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code oo«oooooooo Thread and Block Identification C for CUDA has built-in variables: • threadldx.jx, y, z} tells position of a thread in a block • blockDim.jx, y, z} tells size of the block • blockldx.jx, y, z} tells position of the block in grid (z always equals 1) 9 gridDim.{x, y, z} tells grid size (z always equals 1) Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code OOO0OOOOOOO An Example - Sum of Vectors Thus we calculate the position of the thread (grid and block are one-dimensional): Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code OOO0OOOOOOO An Example - Sum of Vectors Thus we calculate the position of the thread (grid and block are one-dimensional): int i = blockldx.x*blockDim.x + threadldx.x; Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code 000*0000000 An Example - Sum of Vectors Thus we calculate the position of the thread (grid and block are one-dimensional): int i = blockldx.x*blockDim.x + threadldx.x; Whole function for parallel summation of vectors: __global__ void addvec(float *a, float *b , float *c){ int i = biockldx.x*biockDim.x + threadldx.x; c[i] = a[i] + b[i]; } Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code OOO0OOOOOOO An Example - Sum of Vectors Thus we calculate the position of the thread (grid and block are one-dimensional): int i = blockldx.x*blockDim.x + threadldx.x; Whole function for parallel summation of vectors: __global__ void addvec(float *a, float *b , float *c){ int i = biockldx.x*biockDim.x + threadldx.x; c[i] = a[i] + b[i]; } The function defines so called kernel; we specify how meny threads and what structure will be run when calling. Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code OOOO0OOOOOO Function Type Quantifiers C syntax enhanced by quantifiers defining where the code is run and from where it may be called: • __device__ function is run on device (GPU) only and may be called from the device code only • __global__ function is run on device (GPU) only and may be called from the host (CPU) code only • __host__ function is run on host only and may be called from the host only • __host__ and __device__ may be combined - function is compiled for both then Jiří Filipovič Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code oooooooo oooooooo ooooooooo ooooo ooooo«ooooo The following steps are needed for the full computation: Jiří Filipovič Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code oooooooo oooooooo ooooooooo ooooo ooooo«ooooo The following steps are needed for the full computation o allocate memory for vectors and fill it with data □ [31 Jiří Filipovič Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code oooooooo oooooooo ooooooooo ooooo ooooo«ooooo The following steps are needed for the full computation • allocate memory for vectors and fill it with data • allocate memory on GPU Jin Fihpovic rJ1 Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code oooooooo oooooooo ooooooooo ooooo ooooo«ooooo The following steps are needed for the full computation: o allocate memory for vectors and fill it with data • allocate memory on GPU 9 copy vectors a a b to GPU Jiří Filipovič Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code oooooooo oooooooo ooooooooo ooooo ooooo«ooooo The following steps are needed for the full computation o allocate memory for vectors and fill it with data • allocate memory on GPU 9 copy vectors a a b to GPU 9 compute the sum on GPU □ [31 Jiří Filipovič Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code oooooooo oooooooo ooooooooo ooooo ooooo«ooooo The following steps are needed for the full computation o allocate memory for vectors and fill it with data • allocate memory on GPU 9 copy vectors a a b to GPU 9 compute the sum on GPU • store the result from GPU into c □ [31 Jiří Filipovič Introduction, CUDA Basics About The Class Motivation GPU Architecture C for CUDA Sample Code oooooooo oooooooo ooooooooo ooooo ooooo«ooooo The following steps are needed for the full computation: o allocate memory for vectors and fill it with data • allocate memory on GPU 9 copy vectors a a b to GPU 9 compute the sum on GPU • store the result from GPU into c 9 use the result in c :-) When managed memory is used (requires GPU with computing capability 3.0 and CUDA 6.0 or better), steps written in italics are not required. Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code OOOOOO0OOOO An Example - Sum of Vectors CPU code that fills a and b and computes c #include #define N 64 int main(){ float *a, *b, *c ; cudaMallocManaged(&a, N*sizeof(* a ) ) ; cudaMallocManaged(&b, N*sizeof(*b)); cudaMallocManaged(<^c , N*sizeof (* c ) ) ; for (int i = 0; i < N; i++) { a[i] = i; b[i] = i*3; } // GPU code will be here for (int i = 0; i < N; i++) printf("%f, " , c[i]); cudaFree(a); cudaFree(b); cudaFree(c); return 0; } J in Fihpovic Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code 0000000*000 GPU Memory Management Using managed memory, CUDA maintains memory transfers between CPU and GPU automatically. • memory coherency is guaranteed • GPU memory cannot be used when any GPU kernel is running Memory operations can be programmed explicitly cudaMalloc(void** devPtr, size_t count); cudaFree(void* devPtr); cudaMemcpy(void* dst , const void* src , size_t count, enum cudaMemcpyKind kind ) ; Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code OOOOOOOO0OO An Example - Sum of Vectors Running the kernel: • kernel is called as a function; between the name and the arguments, there are triple angle brackets with specification of grid and block size • we need to know block size and their count • we will use ID block and grid with fixed block size • the size of the grid is determined in a way to compute the whole problem of vector sum For vector size divisible by 32: #define BLOCK 32 addvec«(a, b, c); How to solve a general vector size? Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code ooooooooo«o An Example - Sum of Vectors We will modify the kernel source: __global__ void addvec(float *a, float *b, float *c , int n){ int i = biockldx.x*biockDim.x + threadldx.x; if (i < n) c[i] = a[i] + b[i]; } And call the kernel with sufficient number of threads: addvec«»(a, b, c, N) ; Jiří Filipovič Introduction, CUDA Basics About The Class oooooooo Motivation oooooooo GPU Architecture ooooooooo C for CUDA ooooo Sample Code oooooooooo« An Example - Running It Now we just need to compile it :-) nvcc -o vecadd vecadd.cu Where to work with CUDA? • on a remote computer: airacuda.fi.muni.cz, accounts will be made o your own machine: download and install CUDA toolkit and SDK from developer.nvidia.com 9 source code used in lectures will be published as a part of course materials Jiří Filipovič Introduction, CUDA Basics