Introduction
With GPUs and CPUs on the same chip the
memory system becomes more contended for
GPUs typically have high IPCs and are hence
system performance suffers immensely if they
are deprioritized
However, if the GPUs are prioritized for long
periods to ensure high performance then the
other on-chip cores will starve
WARPAL
Our Idea: Identify and
Use Slack Cycles
WARP 1
Compute
GPU CORE
PRESENT ?
NO
STALL :
Issue
WARP 2
Sort GPU Request by Number of Stalled
Warps
WARP 3
Service CPU Request using
TCM/ATLAS/PAR-BS
WARP 4
WARP5
WARP 6
WARP 7
Max. Stall Request
>
Threshold
WARP 8
CPU
STALL
Issue
YES
CPU Request
Present ?
WARP 1
Compute
STALL
Issue 1
2
3
4
WARP 2
YES
Service Request
WARP 3
Architecture
WARP 4
WARP5
Threshold is the number of stalled warps
above which the GPU will be prioritized
It is computed dynamically based on IPC
over the previous epoch
WARP 6
WARP 7
GPU is composed of 16 cores that carry out
FGMT on 32 Warps
CPUs are 3 wide superscalar engines
Individual L1(128KB) caches are present along
with shared L2 cache(2MB)
WARP 8
CPU
STALL:1
Issue
2
3
Number of Non-Stalled Warps and IPC used to
identify slack in the system
Results
1.2
Weighted Speedup
Fairness: Harmonic Speedup
0.006
1
GPU-Core 0
GPU-Core 1
GPU-Core 2
0.005
GPU-Core 3
SS
CPU Core
GPU-Core 4
GPU-Core 5
GPU-Core 6
SS
CPU Core
GPU-Core 7
0.8
0.004
0.6
CPU4
CPU3
0.003
CPU2
0.4
CPU1
GPU
GPU-Core 8
GPU-Core 9
GPU-Core 10
GPU-Core 11
SS
CPU Core
0
0
SSSP_S
SSSP_D
WP_S
WP_D
STMCL_S
STMCL_D
BFS_S
BFS_D
CFD_S
GPU-Core 15
0.001
CFD_D
GPU-Core 14
0.2
BACKP_S
GPU-Core 13
SS
CPU Core
BACKP_D
GPU-Core 12
0.002
Developed mechanism that can be coupled with other memory scheduling algorithms to
increase performance or fairness or both