from __future__ import division import time import os import sys import pyatidlas as atd import numpy as np class PhysicalLimitsNV: def __init__(self, dev): self.compute_capability = dev.nv_compute_capability if self.compute_capability[0]==1: if self.compute_capability[1]<=1: self.warps_per_mp = 24 self.threads_per_mp = 768 self.num_32b_reg_per_mp = 8192 self.reg_alloc_unit_size = 256 else: self.warps_per_mp = 32 self.threads_per_mp = 1024 self.num_32b_reg_per_mp = 16384 self.reg_alloc_unit_size = 512 self.threads_per_warp = 32 self.thread_blocks_per_mp = 8 self.reg_alloc_granularity = 'block' self.reg_per_thread = 124 self.shared_mem_per_mp = 16384 self.shared_mem_alloc_unit_size = 512 self.warp_alloc_granularity = 2 self.max_thread_block_size = 512 elif self.compute_capability[0]==2: self.threads_per_warp = 32 self.warps_per_mp = 48 self.threads_per_mp = 1536 self.thread_blocks_per_mp = 8 self.num_32b_reg_per_mp = 32768 self.reg_alloc_unit_size = 64 self.reg_alloc_granularity = 'warp' self.reg_per_thread = 63 self.shared_mem_per_mp = 49152 self.shared_mem_alloc_unit_size = 128 self.warp_alloc_granularity = 2 self.max_thread_block_size = 1024 elif self.compute_capability[0]==3: self.threads_per_warp = 32 self.warps_per_mp = 64 self.threads_per_mp = 2048 self.thread_blocks_per_mp = 16 self.num_32b_reg_per_mp = 65536 self.reg_alloc_unit_size = 256 self.reg_alloc_granularity = 'warp' if(self.compute_capability[1]==5): self.reg_per_thread = 255 else: self.reg_per_thread = 63 self.shared_mem_per_mp = 49152 self.shared_mem_alloc_unit_size = 256 self.warp_alloc_granularity = 4 self.max_thread_block_size = 1024 elif self.compute_capability[0]==5: #[KR]: copy-pasted from Kepler and adjusted according to http://en.wikipedia.org/wiki/CUDA self.threads_per_warp = 32 self.warps_per_mp = 64 self.threads_per_mp = 2048 self.thread_blocks_per_mp = 32 self.num_32b_reg_per_mp = 65536 self.reg_alloc_unit_size = 256 self.reg_alloc_granularity = 'warp' self.reg_per_thread = 255 self.shared_mem_per_mp = 65536 self.shared_mem_alloc_unit_size = 256 self.warp_alloc_granularity = 4 self.max_thread_block_size = 1024 else: raise Exception('Compute capability not supported!') class PhysicalLimitsAMD: def __init__(self, dev): infos =\ { #APU: 'Devastator': {'arch': 'VLIW', 'WFmax_cu': 96, 'LDS_cu': 32768, 'GPR_cu': 8192}, 'Scrapper': {'arch': 'VLIW', 'WFmax_cu': 96, 'LDS_cu': 32768, 'GPR_cu': 8192}, #HD5000 'Cedar': {'arch': 'VLIW', 'WFmax_cu': 96, 'LDS_cu': 32768, 'GPR_cu': 8192}, 'Redwood': {'arch': 'VLIW', 'WFmax_cu': 62, 'LDS_cu': 32768, 'GPR_cu': 16384}, 'Juniper': {'arch': 'VLIW', 'WFmax_cu': 24.8, 'LDS_cu': 32768, 'GPR_cu': 16384}, 'Cypress': {'arch': 'VLIW', 'WFmax_cu': 27.6, 'LDS_cu': 32768, 'GPR_cu': 16384}, 'Hemlock': {'arch': 'VLIW', 'WFmax_cu': 24.8, 'LDS_cu': 32768, 'GPR_cu': 16384}, #HD6000 'Seymour': {'arch': 'VLIW', 'WFmax_cu': 96, 'LDS_cu': 32768, 'GPR_cu': 16384}, 'Caicos': {'arch': 'VLIW', 'WFmax_cu': 96, 'LDS_cu': 32768, 'GPR_cu': 16384}, 'Turks': {'arch': 'VLIW', 'WFmax_cu': 41.3, 'LDS_cu': 32768, 'GPR_cu': 16384}, 'Whistler': {'arch': 'VLIW', 'WFmax_cu': 41.3, 'LDS_cu': 32768, 'GPR_cu': 16384}, 'Barts': {'arch': 'VLIW', 'WFmax_cu': 49.6, 'LDS_cu': 32768, 'GPR_cu': 16384}, #HD7000 'Capeverde': {'arch': 'GCN', 'WFmax_cu': 40, 'LDS_cu': 65536, 'GPR_cu': 65536}, 'Pitcairn': {'arch': 'GCN', 'WFmax_cu': 40, 'LDS_cu': 65536, 'GPR_cu': 65536}, 'Bonaire': {'arch': 'GCN', 'WFmax_cu': 40, 'LDS_cu': 65536, 'GPR_cu': 65536}, 'Tahiti': {'arch': 'GCN', 'WFmax_cu': 40, 'LDS_cu': 65536, 'GPR_cu': 65536}, #Rx 200 'Oland': {'arch': 'GCN', 'WFmax_cu': 40, 'LDS_cu': 65536, 'GPR_cu': 65536}, 'Tonga': {'arch': 'GCN', 'WFmax_cu': 40, 'LDS_cu': 65536, 'GPR_cu': 65536}, 'Hawaii': {'arch': 'GCN', 'WFmax_cu': 40, 'LDS_cu': 65536, 'GPR_cu': 65536} } self.WFsize = 64 self.WFmax_cu = infos[dev.name]['WFmax_cu'] self.LDS_cu = infos[dev.name]['LDS_cu'] self.GPR_cu = infos[dev.name]['GPR_cu'] self.arch = infos[dev.name]['arch'] pass def _int_floor(value, multiple_of=1): """Round C{value} down to be a C{multiple_of} something.""" # Mimicks the Excel "floor" function (for code stolen from occupancy calculator) from math import floor return int(floor(value/multiple_of))*multiple_of def _int_ceiling(value, multiple_of=1): """Round C{value} up to be a C{multiple_of} something.""" # Mimicks the Excel "floor" function (for code stolen from occupancy calculator) from math import ceil return int(ceil(value/multiple_of))*multiple_of class OccupancyRecord: def init_nvidia(self, dev, threads, shared_mem, registers): pl = PhysicalLimitsNV(dev) limits = [] allocated_warps = max(1,_int_ceiling(threads/pl.threads_per_warp)) max_warps_per_mp = pl.warps_per_mp limits.append((min(pl.thread_blocks_per_mp, _int_floor(max_warps_per_mp/allocated_warps)), 'warps')) if registers>0: if registers > pl.reg_per_thread: limits.append((0, 'registers')) else: allocated_regs = {'warp': allocated_warps, 'block': _int_ceiling(_int_ceiling(allocated_warps, pl.warp_alloc_granularity)*registers*pl.threads_per_warp,allocated_warps)}[pl.reg_alloc_granularity] max_reg_per_mp = {'warp': _int_floor(pl.num_32b_reg_per_mp/_int_ceiling(registers*pl.threads_per_warp, pl.reg_alloc_unit_size), pl.warp_alloc_granularity), 'block':pl.num_32b_reg_per_mp}[pl.reg_alloc_granularity] limits.append((_int_floor(max_reg_per_mp/allocated_regs), 'registers')) if shared_mem>0: allocated_shared_mem = _int_ceiling(shared_mem, pl.shared_mem_alloc_unit_size) max_shared_mem_per_mp = pl.shared_mem_per_mp limits.append((_int_floor(max_shared_mem_per_mp/allocated_shared_mem), 'shared memory')) limit, limited_by = min(limits) warps_per_mp = limit*allocated_warps self.occupancy = 100*warps_per_mp/pl.warps_per_mp def init_amd(self, dev, threads, shared_mem, NReg): pl = PhysicalLimitsAMD(dev) limits = {} WFwg = _int_ceiling(threads/pl.WFsize) #WFmax without constraint if pl.arch=='VLIW': limits['wg'] = pl.WFmax_cu if WFwg > pl.WFmax_cu else _int_floor(pl.WFmax_cu,WFwg) else: limits['wg'] = min(16*WFwg, pl.WFmax_cu) #WFmax with LDS constraints if shared_mem > 0: WGmax = _int_floor(pl.LDS_cu/shared_mem) limits['lds'] = WGmax*WFwg #WFmax with GPR constraints if NReg > 0: #Amount of work group per CU NRegWG = NReg*pl.WFsize*WFwg WGmax = _int_floor(pl.GPR_cu/NRegWG) limits['gpr'] = WFwg*WGmax self.occupancy = 100.0*min(list(limits.values()))/pl.WFmax_cu def __init__(self, dev, threads, shared_mem=0, registers=0): vendor = dev.vendor.lower() if any(X in vendor for X in ['advanced micro devices', 'amd']): self.init_amd(dev, threads, shared_mem, registers) elif 'nvidia' in vendor: self.init_nvidia(dev, threads, shared_mem, registers) elif 'intel' in vendor: self.occupancy = 100 def skip(template, symbolic): device = symbolic.context.queues[0].device array_expressions = atd.array_expression_container(symbolic) registers_usage = template.registers_usage(array_expressions)/4 lmem_usage = template.lmem_usage(array_expressions) local_size = template.local_size_0*template.local_size_1 occupancy_record = OccupancyRecord(device, local_size, lmem_usage, registers_usage) if template.check_invalid(array_expressions, device) or occupancy_record.occupancy < 15: return True return False def benchmark(template, symbolic): queue = symbolic.context.queues[0] device = queue.device array_expressions = atd.array_expression_container(symbolic) registers_usage = template.registers_usage(array_expressions)/4 lmem_usage = template.lmem_usage(array_expressions) local_size = template.local_size_0*template.local_size_1 occupancy_record = OccupancyRecord(device, local_size, lmem_usage, registers_usage) if occupancy_record.occupancy < 15 : raise ValueError("Template has too low occupancy") else: queue.models[template, atd.float32] = atd.model(template, queue) x, events, cache = atd.flush(symbolic) atd.synchronize(symbolic.context) timings = [] current_time = 0 while current_time < 1e-3: x, events, cache = atd.flush(symbolic) atd.synchronize(symbolic.context) timings.append(1e-9*sum([e.end - e.start for e in events])) current_time = current_time + timings[-1] return np.median(timings) def sanitize_string(string, keep_chars = ['_']): string = string.replace(' ', '_').replace('-', '_').lower() string = "".join(c for c in string if c.isalnum() or c in keep_chars).rstrip() return string