When analyzing job performance in GridEngine (now known as Univa Grid Engine or Altair Grid Engine), you'll encounter three fundamental time measurements:

# Sample GridEngine output format
=============================================
Wallclock Time: 00:05:23
User Time: 00:03:41
CPU Time: 00:04:12
=============================================

Wall clock time (or elapsed time) represents the actual time taken from job start to completion, as if you were timing it with a physical stopwatch. This includes:

• All system delays (I/O waits)
• Network latency
• Queue waiting time
• Other processes' interference

Example scenario where wall time differs significantly:

# Python script with sleep
import time

start = time.time()
# Computation
time.sleep(10)  # Artificial delay
# More computation
end = time.time()
print(f"Wall time: {end - start:.2f} seconds")

User time accounts for the CPU time spent executing your application's code in user space. Key characteristics:

• Excludes system calls and kernel operations
• Multi-core systems may report values exceeding wall time
• Best for single-threaded performance comparison

Example using Linux time command:

$ time -p my_application
real 4.23  # Wall time
user 3.78  # User time
sys 0.32   # System time

CPU time (often called system time) includes both user time and time spent in kernel operations:

• Includes system calls and I/O operations
• For multi-threaded apps, can exceed wall time
• Important for analyzing system call overhead

Demonstration in C:

#include <sys/times.h>
#include <unistd.h>

void main() {
    struct tms t;
    times(&t);
    // t.tms_utime = user CPU time
    // t.tms_stime = system CPU time
}

For benchmarking applications:

Metric	Best Use Case	Limitations
Wall Clock	End-to-end system performance	Affected by external factors
User Time	Algorithm efficiency	Misses system overhead
CPU Time	Total resource usage	Hard to isolate app-specific cost

Practical GridEngine monitoring example:

# qacct output analysis
qacct -j job_id | egrep "wallclock|ru_utime|ru_stime"

When dealing with parallel jobs:

# MPI job with 4 processes might show:
Wallclock: 60s
User Time: 220s (4 cores × 55s each)
CPU Time: 240s (includes system overhead)

For accurate comparisons:

1. Run multiple iterations
2. Use dedicated test nodes
3. Monitor system load averages
4. Consider using dedicated benchmarking tools like HPL

---

When analyzing computing jobs in GridEngine (now known as Univa Grid Engine or Altair Grid Engine), we encounter three fundamental time metrics that reveal different aspects of execution performance:

# Sample GridEngine output format
qacct -j job_id | egrep "wallclock|ru_utime|ru_stime"
wallclock   00:05:23
ru_utime    00:03:45
ru_stime    00:01:12

Wall clock time (also called elapsed time) measures the total duration from job start to completion, including all system overhead and waiting periods. This is what you'd measure with a physical stopwatch.

Example scenario: A Python script that makes API calls:

import time
start = time.time()
# API call with network latency
response = requests.get('https://api.example.com/data') 
end = time.time()
print(f"Wall time: {end - start:.2f}s")

User CPU time (ru_utime) accounts for time spent executing application code in user space. Multiple threads/cores can cause this to exceed wall time.

// C program showing user vs system time
#include 
#include 

int main() {
    clock_t start = clock();
    // CPU-intensive user operations
    for(long i=0; i<1000000000; i++);
    clock_t end = clock();
    printf("User CPU time: %.2f sec\n", 
           (double)(end - start)/CLOCKS_PER_SEC);
    return 0;
}

Total CPU time (user + system time) represents all processor cycles consumed. System time (ru_stime) covers kernel operations like I/O handling.

Key differences in database operations:

• Wall time: Includes connection latency
• User time: Query processing only
• System time: Disk I/O operations

For comparing application performance:

1. Algorithm efficiency: Use user CPU time (eliminates system variance)
2. Real-world impact: Wall clock time matters for SLA compliance
3. Resource usage: Total CPU time reveals optimization opportunities

Advanced analysis example using Unix time command:

$ /usr/bin/time -v python script.py
Command being timed: "python script.py"
User time (seconds): 12.45
System time (seconds): 1.87
Percent of CPU this job got: 198%
Elapsed (wall clock) time (h:mm:ss or m:ss): 0:07.21

When submitting jobs with qsub, be aware that:

• Wall time limits are enforced via -l h_rt=HH:MM:SS
• CPU time accounting depends on scheduler configuration
• Multi-core jobs aggregate CPU time across all cores