Introduction CUDA OCL AMD GPU Architecture oooo ooooooooo ooooooooooo OpenCL J-W I- ■ I " "V in Fihpovic fall 2018 Jin Filipovic OpenCL Introduction •ooo CUDA OCL ooooooooo AMD GPU Architecture ooooooooooo OpenCL What is OpenCL? • an open standard for heterogeneous systems programming <* low-level, derived from C, HW abstraction very similar to CUDA Advantages over CUDA • can be used for wide area of HW 9 open standard, independent on a single corporation Disadvantages compared to CUDA • more complex API (similar to CUDA Driver API) • often less mature implementation • slower implementation of new HW features Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture o«oo ooooooooo ooooooooooo Portability One implementation can be compiled for different types of HW o if we do not use extensions ... However, the implementation optimized for some type of HW may be very slow on another HW o we need to re-optimize for different HW architectures So, it is the standard for programming of various types of HW, but we need to write different kernels for different architectures. • high importance easily modifiable code or autotuning Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture oo«o ooooooooo ooooooooooo Performance Portability 600 rN^U3COOrN^^«)OrN^U3«>Or\l^lOOOOrN*±lDOOOrN^-ir>COOrN 01MrviXi^in^r^rNrNtHOaiK)00r--LDLO^t^-r0fNt-IOOtT100rvLDiXiLn H^Ou1^01Hf*lln^mH^l^lDWOl^l■*^^1000rS'l^DMO\riff|lfl^Sl rlrlririrliNMNrNrNrororfimm^^^^^^inifli/li/lifl N Obrazek : SGEMM optimized for Fermi and Cypress, running on Fermi Du et al. From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming J in Fihpovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture ooo» ooooooooo ooooooooooo Performance Portability 1600 iN^iD(»OiS^iO(»OM<}iD«IOM^iO(»ON^iO(i30rN!}iI)«IOrN Oiecr^LD^LTi^rr)fNry|^oaiMMr^^LO^^rOfNrHOOa,iOOr^tDiriLri rHrriL0r^OTtHmLnr^CTl^rO^^C>0OrN^^00OiNrtLDC>0CTlt-lrniOr--ai N Obrazek : SGEMM optimized for Fermi a Cypress, running on Cypress2. Du et al. From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming J in Fihpovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture OOOO «00000000 ooooooooooo Main Differences OpenCL is not integrated to C/C++ • the OpenCL kernel is stored as a string, which is usually compiled during program execution • kernel cannot share code with C/C++ codebase (user-defined types, common functions etc.) Kernels in OpenCL do not use pointers • we cannot dereference, use pointer arithmetics, link different buffers o we can traverse the buffer by index, of course OpenCL is strictly derived from C • no C++ stuff OpenCL uses queues for HW devices • eases using multiple devices/streams Queues can work out-of-order • eases load balancing J in Fihpovic OpenCL Introduction oooo CUDA OCL O0OOOOOOO AMD GPU Architecture ooooooooooo CUDA-OpenCL dictionary Main differences in terminology CUDA OpenCL multiprocessor compute unit scalar processor processing element thread work-item thread block work-group grid ND Range shared memory local memory registers private memory J in Fihpovic OpenCL □ [31 Introduction oooo CUDA OCL OO0OOOOOO AMD GPU Architecture ooooooooooo Vector Addition - Kernel CUDA __global__ void addvec(float *a, float *b , float *c) { int i = biockldx.x*biockDim.x + threadldx.x; c[i] = a[i] + b[i]; } OpenCL ..kernel void ve cadd („global float * a, „global float * b, „global float * c) { int i = get_global_id(0); c[i] = a[i] + b[i]; } Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture OOOO OOO0OOOOO ooooooooooo Vector Addition - Host Code To execute the kernel, we need • to define a platform • device (at least one) • context • queues a allocate and copy data • compile the kernel code • configure the kernel and execute it Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture oooo oooo«oooo ooooooooooo Vector Addition - Platform Definition cl_uint num_devices_returned; cl_device_id cdDevice; err = clGetDeviceIDs(NULL , CL_DEVICE_TYPE_GPU , 1, &cdDevice , <^num_devices_returned ) ; cl_context hContext ; hContext = clCreateContext(0 , 1, &cdDevice , NULL, NULL, ^err); cl_command_queue hQueue; hQueue = clCreateCommandQueue(hContext , hDevice , 0, &err ) ; Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture oooo ooooo«ooo ooooooooooo Vector Addition - Platform Definition The platform can have more devices • can be selected by the type (e.g. a GPU) • can be selected by vendor • we can also choose HW using finer informations • number of cores • frequency • memory size • extensions (double precision, atomic operations etc.) Each device needs at least one queue • cannot be used otherwise Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture OOOO OOOOOO0OO ooooooooooo Vector Addition - Memory Allocation and Copy cl_mem hdA , hdB , hdC ; hdA = clCreateBuffer(hContext , CL_MEM_READ_ONLY , cnDimension * sizeof(cl_float), pA, 0); There is no explicit copy - allocation and copy is performed in lazy fashion, i.e. in time when data are needed. Consequently, the target device is not defined in the memory allocation. Jin Filipovic OpenCL Introduction oooo CUDA OCL ooooooo«o AMD GPU Architecture ooooooooooo Vector Addition - Kernel Execution const unsigned int cnBlockSize = 512; const unsigned int cnBlocks = 3; const unsigned int cnDimension = cnBlocks * cnBlockSize; cl_program hProgram; hProgram = clCreateProgramWithSource(hContext, 1, sProgramSource ,0, 0); clBuildProgram(hProgram, 0, 0, 0, 0, 0); cl_kernel hKernel; hKernel = clCreateKernel(hProgram , "addvec" , 0); clSetKernelArg(hKernel , 0, sizeof(cl_mem) , (void *)&hdA ) clSetKernelArg(hKernel, 1, sizeof(cl_mem), (void *)&hdB) clSetKernelArg(hKernel, 2, sizeof(cl_mem), (void *)&hdC) clEnqueueNDRangeKernel(hQueue, hKernel, 1, 0, <^cnDimension , ^cnBlockSize, 0, 0, 0); Jin Filipovic OpenCL Introduction CUDA OCL AMD GPU Architecture oooo oooooooo* ooooooooooo Vector Addition - Cleanup clReleaseKernel(hKernel ) ; clReleaseProgram(hProgram ) ; clReleaseMemObj (hdA ) ; clReleaseMemObj (hdB ) ; clReleaseMemObj(hdC); clReleaseCommandQueue(hQueue ) ; cIReleaseContext(hContext ); Jin Fihpovic OpenCL □ Introduction CUDA —► OCL AMD GPU Architecture OOOO OOOOOOOOO «0000000000 AMD VLIW GPU Architecture Older processors • Evergreen and Northern Islands We will discuss main differences between AMD and NVIDIA GPU • the rest is very similar Main differences • VLIW architecture 9 two memory access modes - the fast path and complete path less sensitive to misaligned access, more sensitive to partition camping analogy • wavefront (the warp analogy) has 64 threads Jiŕí Filipovič OpenCL Introduction CUDA —► OCL AMD GPU Architecture OOOO OOOOOOOOO O0OOOOOOOOO VLIW Architecture VLIW • the instruction word includes several independent operations • static planning of instruction parallelism (dependencies analyzed during compilation) • allows higher density of ALUs • threads should perform a code with sufficient instruction parallelism and a compiler needs to recognize it • easier in typical graphics tasks than general computating ones • AMD GPU implements VLIW-5 or VLIW-4, 1 instruction is SFU Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture OOOO OOOOOOOOO 00*00000000 Optimizations for VLIW Explicit vectorization o we work with vector variables (e.g. float4) • generation of VLIW is straightforward for the compiler Implicit generation of VLIW • we write a scalar code 9 compiler tries to recognize independent instruction and create VLIW code o we can help the compiler by unrolling and grouping the same operations performing different iterations Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture OOOO OOOOOOOOO OOO0OOOOOOO Optimizations for VLIW Issues with VLIW • higher consumption of on-chip resources per thread (unrolling, vector types) • we need independent instructions • problematic e.g. with conditions • together with large wavefront it is highly sensitive to divergence Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture oooo ooooooooo oooo«oooooo Global Memory Access Fast path vs. complete path a fast path is significantly faster • fast path is used for load/store of 32-bit values • complete path is used for everything other (values of different size, atomics) • the compiler needs to explicitly use one of those paths • access path is the same for the whole buffer, so we can degrade the global memory bandwidth easily Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture OOOO OOOOOOOOO 00000*00000 Fast path vs. complete path ..kernel void CopyComplete(__global const float * input, __global float* output) { int gid = get_global_id(0 ) ; if (gid < 0){ atom.add((__global int *) output ,1); } output[gid] = input[gid]; return ; } Difference on Radeon HD 5870: 96GB/s vs. 18GB/s. Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture OOOO OOOOOOOOO OOOOOO0OOOO Global Memory Access Permutation of thread-element mapping in wavefront • small penalization (< 10%) 9 better than c.c. < 1.2 Faster access using 128-bit in single instruction • e.g. accessing float4 • 122GB/s instead 96GB/s using HD 5870 and the memory copy example Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture OOOO OOOOOOOOO 0000000*000 Memory Channels Radeons of 5000 series have memory channels interleaved by 256 bytes o all threads within wavefront should use the same channel wavefront accessing the aligned contiguous block of 32-bit elements (with arbitrary permutation of thread-element mapping) uses the same channel • if multiple channels are accessed by wavefront, the access is serialized • occurs e.g. in misaligned access Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture OOOO OOOOOOOOO OOOOOOOO0OO Bank and Channel Conflicts Analogy of partition camping • the global memory is accessed using banks and channels • concurrent workgroups should access via different channels and different banks • bandwidth is limited otherwise • the arrangement of banks depends on the number of channels • for instance, 8 channels means that the bank switches every 2KB • high penalization of accessing the same channel and the same bank (0.3 vs. 93GB/s on Radeon HD 5870) Jin Filipovic OpenCL Introduction oooo Local Data Storage CUDA OCL ooooooooo AMD GPU Architecture ooooooooo«o Local Data Storage (LDS) is very similar to NVIDIA's shared memory a composed of 32 or 16 banks • the quarter-wafefront needs to access different banks simultaneously o otherwise the bank conflicts appear • in the case of 32 banks we can efficiently use float2 • broadcast is supported for a single value (analogy of c.c. 1.x) Jin Filipovic OpenCL Introduction CUDA —► OCL AMD GPU Architecture oooo ooooooooo oooooooooo* AMD GCN GPU Architecture Nowadays architecture, known as Graphic Core Next. Significantly different than previous generations • no VLIW, compute unit contains one scalar processor and four vector processors the code performed by threads is scalar (vectorized code usually slower because of resource consumption) • conditions penalization is lower compared to VLIW • LI cache for read and write • concurrent kernel invocations Jin Filipovic OpenCL