cc2_3cke


scheme:

  • 1) for data transfer, pick 1st sleep api (h2d) fo stream-0, current cc = 1 (concurrency),
  • 2) check whether there is overalp with stream-
  • 2) if there is overlap, finish cc=1, start from cc++ (cc=2), predit the future ending time
  • 3) during the predicted ending time, check whether there is overlap with stream-2
  • 4) if there is overalap, finish cc=2, start from cc++ (cc=3), predict the future ending time
  • 5) go to step 3) , search through all the cuda streams
  • 6) for each time range, we need to find out how many apis have overlap and which-pair have conflicts or not

In [1]:
%load_ext autoreload
%autoreload 2

import warnings
import pandas as pd
import numpy as np
import os
import sys # error msg, add the modules
import operator # sorting
from math import *
import matplotlib.pyplot as plt

sys.path.append('../../')

import cuda_timeline
import read_trace
import avgblk
import cke
from model_param import *
#from df_util import *

warnings.filterwarnings("ignore", category=np.VisibleDeprecationWarning)

gpu info


In [2]:
gtx950 = DeviceInfo()
gtx950.sm_num = 6
gtx950.sharedmem_per_sm = 49152
gtx950.reg_per_sm = 65536
gtx950.maxthreads_per_sm = 2048

In [3]:
# init SM resources
SM_resList, SM_traceList = init_gpu(gtx950)

In [4]:
#SM_resList[0]

In [5]:
SM_traceList[0]


Out[5]:
sm_id block_id block_start block_end batch_id kernel_id active

Understand the input


In [6]:
trace_s1 = 'trace_s1_5m.csv'
df_trace_s1 = read_trace.Trace2dataframe(trace_s1)

trace_s3 = 'trace_s3_5m.csv'
df_trace_s3 = read_trace.Trace2dataframe(trace_s3)

In [7]:
#df_trace_s1

In [8]:
cuda_timeline.plot_trace(df_trace_s1)


/home/leiming/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py:1292: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  if aspect == 'normal':
/home/leiming/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.py:1297: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  elif aspect in ('equal', 'auto'):

In [9]:
#cuda_timeline.plot_trace(df_trace_s2)

In [10]:
cuda_timeline.plot_trace(df_trace_s3)


Kernel Info from the single stream


In [11]:
# extract kernel info from trace
# warning: currently lmted to one kernel

kernel = read_trace.GetKernelInfo(df_trace_s1, gtx950)

Dump_kernel_info(kernel)


Kernel Info
		blockDim 256.0
		gridkDim 19532.0
		regs 28.0
		shared memory 0.0
		runtime (ms) 11.914429
		average block execution time (ms) 0.0292737813268
		start time (ms) 0

model 3 cuda streams


In [12]:
# for each stream, have a dd for each kernel
stream_kernel_list = []

stream_num = 3

for sid in range(stream_num):
    #print sid
    # key will be the kernel order
    # value will be the kernel info
    kern_dd = {}
    kern_dd[0] = Copy_kernel_info(kernel)
    stream_kernel_list.append(kern_dd)
    
    
Dump_kernel_info(stream_kernel_list[0][0])


Kernel Info
		blockDim 256.0
		gridkDim 19532.0
		regs 28.0
		shared memory 0.0
		runtime (ms) 11.914429
		average block execution time (ms) 0.0292737813268
		start time (ms) 0

start kernel from beginning


In [13]:
df_s1_trace_timing = read_trace.Get_timing_from_trace(df_trace_s1)
df_s1 = read_trace.Reset_starting(df_s1_trace_timing)

In [14]:
df_s1


Out[14]:
stream api_type start end size duration
0 0.0 h2d 0.000000 3.187298 19073.486 3.187298
1 0.0 h2d 3.188514 6.384227 19073.486 3.195713
2 0.0 kern 6.391203 18.305632 0.000 11.914429
3 0.0 d2h 18.311008 21.445857 19073.486 3.134849

set the h2d start for all the cuda streams


In [15]:
# find when to start the stream and update the starting pos for the trace
H2D_H2D_OVLP_TH = 3.158431

df_cke_list = cke.init_trace_list(df_s1, stream_num = stream_num, h2d_ovlp_th = H2D_H2D_OVLP_TH)


stream_startTime : 3.160431
stream_startTime : 6.320862

In [16]:
df_cke_list[0]


Out[16]:
stream api_type start end size duration
0 0 h2d 0.000000 3.187298 19073.486 3.187298
1 0 h2d 3.188514 6.384227 19073.486 3.195713
2 0 kern 6.391203 18.305632 0.000 11.914429
3 0 d2h 18.311008 21.445857 19073.486 3.134849

In [17]:
df_cke_list[1]


Out[17]:
stream api_type start end size duration
0 1 h2d 3.160431 6.347729 19073.486 3.187298
1 1 h2d 6.348945 9.544658 19073.486 3.195713
2 1 kern 9.551634 21.466063 0.000 11.914429
3 1 d2h 21.471439 24.606288 19073.486 3.134849

In [18]:
df_cke_list[2]


Out[18]:
stream api_type start end size duration
0 2 h2d 6.320862 9.508160 19073.486 3.187298
1 2 h2d 9.509376 12.705089 19073.486 3.195713
2 2 kern 12.712065 24.626494 0.000 11.914429
3 2 d2h 24.631870 27.766719 19073.486 3.134849

merge all the cuda stream trace together


In [19]:
df_all_api = cke.init_sort_api_with_extra_cols(df_cke_list)

In [20]:
df_all_api


Out[20]:
start end api_type size_kb stream_id status bw bytes_done bytes_left current_pos pred_end
0 0.000000 3.187298 h2d 19073.486 0.0 sleep 5984.217980 0.0 19073.486 0.0 0.0
4 3.160431 6.347729 h2d 19073.486 1.0 sleep 5984.217980 0.0 19073.486 0.0 0.0
1 3.188514 6.384227 h2d 19073.486 0.0 sleep 5968.460247 0.0 19073.486 0.0 0.0
8 6.320862 9.508160 h2d 19073.486 2.0 sleep 5984.217980 0.0 19073.486 0.0 0.0
5 6.348945 9.544658 h2d 19073.486 1.0 sleep 5968.460247 0.0 19073.486 0.0 0.0
2 6.391203 18.305632 kern 0.000 0.0 sleep 0.000000 0.0 0.000 0.0 0.0
9 9.509376 12.705089 h2d 19073.486 2.0 sleep 5968.460247 0.0 19073.486 0.0 0.0
6 9.551634 21.466063 kern 0.000 1.0 sleep 0.000000 0.0 0.000 0.0 0.0
10 12.712065 24.626494 kern 0.000 2.0 sleep 0.000000 0.0 0.000 0.0 0.0
3 18.311008 21.445857 d2h 19073.486 0.0 sleep 6084.339628 0.0 19073.486 0.0 0.0
7 21.471439 24.606288 d2h 19073.486 1.0 sleep 6084.339628 0.0 19073.486 0.0 0.0
11 24.631870 27.766719 d2h 19073.486 2.0 sleep 6084.339628 0.0 19073.486 0.0 0.0

start algorithm


In [21]:
# stream_id list
stream_list = [float(x) for x in range(stream_num)]

# pick the 1st sleep api
df_all_api, r1, r1_stream = cke.pick_base_call(df_all_api)
df_all_api = SetWake(df_all_api, r1)
print('row {}, stream-id {}'.format(r1, r1_stream))


# select coming (stream_num - 1) api calls, they are potential concurrency api calls
cc_rows = FindComingCalls(df_all_api, r1, stream_num)


count = 1

active_stream_pool = []
active_stream_pool.append(r1_stream)

for r2 in cc_rows:
    print('active_stream_pool : {}'.format(active_stream_pool))
    #if count == 1: break
    #if count == 2: break
    
    # wake target row
    df_all_api = SetWake(df_all_api, r2)
    r2_stream = GetStreamID(df_all_api, r2)
    print('row {}, stream-id {}'.format(r2, r2_stream))
    
    
    if count == 2: break
        
    # check whether current stream is in the active stream pool
    # where I mean they are from the same stream
    if r2_stream in active_stream_pool:
        # finish previous call
        df_all_api = cke.finish_call(df_all_api, r1)
        # update the time for all the calls in this stream
        df_all_api = cke.UpdateStreamTime(df_all_api)
        # adjust the r2 current pos and pred_end
        df_all_api = UpdateCell(df_all_api, r2, 'current_pos', GetInfo(df_all_api, r2, 'start'))
        df_all_api = UpdateCell(df_all_api, r2, 'pred_end', GetInfo(df_all_api, r2, 'end'))
        break
        
        
    # if it is a new stream, add to the pool
    active_stream_pool.append(r2_stream)
    
    
    #---------------------------------------
    # move the current pos to the r2 start  (to do)
    #---------------------------------------
    
    
    
    #if count == 1: break
    #if count == 2: break
    
#     rangeT = cke.Get_pred_range(df_all_api)
#     print rangeT
    
#     # check whether there is conc during the rangeT
#     cke.Check_ovlp(df_all_api, r1, r2)
    
    
    
    
    #break
    
    
    
    # when they are from different streams
    
    #-----------------------
    # move the current_pos to the starting of coming api r2, and update r1 status
    #-----------------------
    #df_all_api = cke.StartNext_byType(df_all_api, [r1, r2])
    
    #break
    
    df_all_api = cke.MoveCurPos(df_all_api, r2) # start r2, check concurrency,and update the timing
    
    #if count == 1: break
    #if count == 2: break
        
    #break
    
    
    #-----------------------------
    # if one call is done, continue the next round
    #-----------------------------
    rowDone, rowDone_list = cke.CheckRowDone(df_all_api, [r1, r2])
    if rowDone:
        print('soem call is ended, go for next call')
        print rowDone_list
        
        for each in rowDone_list: # throw away the done stream
            active_stream_pool = [x for x in active_stream_pool if x <> each]
            
        print active_stream_pool
        
        count += 1
        
        #break
        continue
        
    
    whichType = cke.CheckType(df_all_api, r1, r2) # check whether the same api
    print whichType
    
    if whichType == None:
        # run noconflict
        pass
    elif whichType in ['h2d', 'd2h']: # data transfer in the same direction
        df_all_api = cke.Predict_transferOvlp(df_all_api, [r1, r2])
        if count == 1: break
        #break
    else:
        # concurrent kernel: todo
        pass
    
    #--------------------
    # check if any api is done, and update the timing for the other apis in that stream
    #--------------------
    df_all_api = cke.UpdateStreamTime(df_all_api)

    count += 1
    #break

    
## after checking the commming call for ovlp


row 0, stream-id 0.0
active_stream_pool : [0.0]
row 4, stream-id 1.0
from 0.0 to 3.160431, cc = 1.0
[0]
row 0, end 3.187298, my_curpos 0.0, my_pred_end 3.187298, my_left_new 160.777984476
from 3.160431 to 3.187298, cc = 2.0
[0, 4]
new pred range from 3.160431 to 3.214165
row 0, end 3.187298, my_curpos 3.160431, my_pred_end 3.214165, my_left_new -6.8212102633e-13
soem call is ended, go for next call
[0]
[1.0]
active_stream_pool : [1.0]
row 1, stream-id 0.0

In [22]:
df_all_api


Out[22]:
start end api_type size_kb stream_id status bw bytes_done bytes_left current_pos pred_end
0 0.000000 3.214165 h2d 19073.486 0.0 done 5984.217980 19073.486 0.000 3.214165 3.214165
4 3.160431 6.347729 h2d 19073.486 1.0 wake 5984.217980 0.000 19073.486 3.160431 9.535027
1 3.215381 6.411094 h2d 19073.486 0.0 wake 5968.460247 0.000 19073.486 3.215381 6.411094
8 6.320862 9.508160 h2d 19073.486 2.0 sleep 5984.217980 0.000 19073.486 0.000000 0.000000
5 6.348945 9.544658 h2d 19073.486 1.0 sleep 5968.460247 0.000 19073.486 0.000000 0.000000
2 6.418070 18.332499 kern 0.000 0.0 sleep 0.000000 0.000 0.000 0.000000 0.000000
9 9.509376 12.705089 h2d 19073.486 2.0 sleep 5968.460247 0.000 19073.486 0.000000 0.000000
6 9.551634 21.466063 kern 0.000 1.0 sleep 0.000000 0.000 0.000 0.000000 0.000000
10 12.712065 24.626494 kern 0.000 2.0 sleep 0.000000 0.000 0.000 0.000000 0.000000
3 18.337875 21.472724 d2h 19073.486 0.0 sleep 6084.339628 0.000 19073.486 0.000000 0.000000
7 21.471439 24.606288 d2h 19073.486 1.0 sleep 6084.339628 0.000 19073.486 0.000000 0.000000
11 24.631870 27.766719 d2h 19073.486 2.0 sleep 6084.339628 0.000 19073.486 0.000000 0.000000

In [23]:
#
# run above
#