Can someone explain the meaning of frontend and backend stalled cycles in perf stat output?

Frontend
1

I’ve been looking at the output of perf stat and I’m confused about two specific metrics: stalled-cycles-frontend and stalled-cycles-backend. What do these actually mean? I’ve tried searching online but couldn’t find a clear explanation.

Here’s an example of the output I’m seeing:

$ perf stat my_program

Performance stats for 'my_program':

      1.234567 task-clock                #    0.789 CPUs utilized          
        123456 cycles                    #    1.000 GHz                    
        234567 stalled-cycles-frontend   #  190.00% frontend cycles idle   
        345678 stalled-cycles-backend    #  280.00% backend  cycles idle
        456789 instructions              #    3.70  insns per cycle        
                                         #    0.51  stalled cycles per insn

   0.001234567 seconds time elapsed

Can anyone break down what these frontend and backend stalled cycles actually represent? How do they relate to CPU performance? Thanks for any help!

2

frontend stalled cycles r when CPU waits 4 instructions, like cache misses or branch prediction fails. backend stalled cycles happen when CPU cant execute instructions, maybe due 2 resource conflicts or data dependencies. both slow down ur program, but frontend stalls r often easier 2 fix. hope that helps!

3

Frontend and backend stalled cycles are indeed crucial performance metrics. Frontend stalls occur when the CPU’s instruction fetch and decode units are idle, often due to instruction cache misses or branch mispredictions. Backend stalls, on the other hand, happen when the execution units can’t process instructions, typically because of data dependencies or resource contention.

In your output, the high percentages (190% and 280%) indicate significant stalling in both areas. This suggests potential optimization opportunities. You might want to examine your code for memory access patterns, improve branch prediction, or restructure algorithms to reduce dependencies. Profiling tools can help pinpoint specific bottlenecks causing these stalls.

4

hey, those numbers are pretty interesting! have u tried running ur program with different inputs? i’m curious if the stalled cycles change much. maybe there’s a specific part of ur code that’s causing these stalls? it’d be cool to see how different optimizations affect those percentages. what kind of program is it, btw?