[SYSTEMDS-619] Same usage of same random matrix leads to different results

• Type: Bug
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Affects Version/s: SystemML 0.9, SystemML 0.10
• Fix Version/s: SystemML 0.9, SystemML 0.10
• Assignee: Matthias Boehm
• Reporter: Mike Dusenberry
• Created: 06/Apr/16 20:39
• Updated: 08/Apr/16 17:23
• Resolved: 08/Apr/16 17:22

Description

Interesting bug, as of commit e16fe1d :

Start by creating a random matrix X, multiplying it by 2 and assigning to a variable dX, and then print the results:

# Generate data
N = 3
D = 2
X = rand(rows=N, cols=D)

# Function
dX = 2 * X

# Print elements of dX
for (i in 1:nrow(dX)) {
  for (j in 1:ncol(dX)) {
    print("dX["+i+","+j+"]: " + as.scalar(dX[i,j]))
  }
}
print("")

print("")
print("")
print("")

Output:

dX[1,1]: 1.0743268190621265
dX[1,2]: 1.403590780383033
dX[2,1]: 1.9404746268735837
dX[2,2]: 0.8689030633611705
dX[3,1]: 0.2589227727050818
dX[3,2]: 0.342402157694327

Now, copy and paste the assignment to dX and the print statement, thus literally repeating the same code again.

# Generate data
N = 3
D = 2
X = rand(rows=N, cols=D)

# Function
dX = 2 * X

# Print elements of dX
for (i in 1:nrow(dX)) {
  for (j in 1:ncol(dX)) {
    print("dX["+i+","+j+"]: " + as.scalar(dX[i,j]))
  }
}
print("")

# Function
dX = 2 * X

# Print elements of dX
for (i in 1:nrow(dX)) {
  for (j in 1:ncol(dX)) {
    print("dX["+i+","+j+"]: " + as.scalar(dX[i,j]))
  }
}
print("")

print("")
print("")
print("")

Output:

dX[1,1]: 1.5299997333070705
dX[1,2]: 1.951679510186853
dX[2,1]: 0.9372371721327426
dX[2,2]: 0.11462997451231827
dX[3,1]: 0.3913879515630596
dX[3,2]: 0.4411374996556454

dX[1,1]: 0.15757825641372136
dX[1,2]: 1.6331143898957619
dX[2,1]: 0.7271506546939133
dX[2,2]: 0.648694276576909
dX[3,1]: 1.4763697903577369
dX[3,2]: 1.2645782773949483

Notice that the outputs are different... magic!

Issue Links

• relates to: SYSTEMDS-587 Improvements Triggered By Deep Learning Work