237 lines
10 KiB
Python
237 lines
10 KiB
Python
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
|