GPU Arch Microbenchmark

Prerequisites

1. install turingas compiler

   git clone --recursive git@github.com:sjfeng1999/gpu-arch-microbenchmark.git
cd turingas
python setup.py install

Usage

1. mkdir build && cd build
2. cmake .. && make
3. python ../compile_sass.py -arch=(70|75|80)
4. ./(memory_latency|reg_bankconflict|...)

Microbenchmark

1. Memory Latency

Device	Latency	Turing RTX-2070 (TU104)
Global Latency	cycle	1000 ~ 1200
TLB Latency	cycle	472
L2 Latency	cycle	236
L1 Latency	cycle	32
Shared Latency	cycle	23
Constant Latency	cycle	448
Constant L2 Latency	cycle	62
Constant L1 Latency	cycle	4

• const L1-cache is as fast as register.

2. Memory Bandwidth

1. memory bandwidth within one thread

Device	Bandwidth	Turing RTX-2070
Global LDG.128	GB/s	194.12
Global LDG.64	GB/s	140.77
Global LDG.32	GB/s	54.18
Shared LDS.128	GB/s	152.96
Shared LDS.64	GB/s	30.58
Shared LDS.32	GB/s	13.32

1. global memory bandwidth within (64 block * 256 thread)

Device	Bandwidth	Turing RTX-2070
LDG.32	GB/s	246.65
LDG.32 Group1 Stride1	GB/s	118.73(2X)
LDG.32 Group2 Stride2	GB/s	119.08(2X)
LDG.32 Group4 Stride4	GB/s	117.11(2X)
LDG.32 Group8 Stride8	GB/s	336.27
LDG.64	GB/s	379.24
LDG.64 Group1 Stride1	GB/s	126.40(2X)
LDG.64 Group2 Stride2	GB/s	124.51(2X)
LDG.64 Group4 Stride4	GB/s	398.84
LDG.64 Group8 Stride8	GB/s	371.28
LDG.128	GB/s	391.83
LDG.128 Group1 Stride1	GB/s	125.25(2X)
LDG.128 Group2 Stride2	GB/s	402.55
LDG.128 Group4 Stride4	GB/s	394.22
LDG.128 Group8 Stride8	GB/s	396.10

3. Cache Linesize

Device	Linesize	Turing RTX-2070(TU104)
L2 Linesise	bytes	64
L1 Linesize	bytes	32
Constant L2 Linesise	bytes	256
Constant L1 Linesize	bytes	32

4. Reg Bankconflict

Instruction	CPI	conflict	without conflict	reg reuse	double reuse
FFMA	cycle	3.516	2.969	2.938	2.938
IADD3	cycle	3.031	2.062	2.031	2.031

5. Shared Bankconflict

Memory Load	Latency	Turing RTX-2070 (TU104)
Single	cycle	23
Vector2 X 2	cycle	27
Conflict Strided	cycle	41
Conlict-Free Strided	cycle	32

Instruction Efficiency

Roadmap

• warp schedule
• L1/L2 cache n-way k-set

Citation

• Jia, Zhe, et al. "Dissecting the NVIDIA volta GPU architecture via microbenchmarking." arXiv preprint arXiv:1804.06826 (2018).
• Jia, Zhe, et al. "Dissecting the NVidia Turing T4 GPU via microbenchmarking." arXiv preprint arXiv:1903.07486 (2019).
• Yan, Da, Wei Wang, and Xiaowen Chu. "Optimizing batched winograd convolution on GPUs." Proceedings of the 25th ACM SIGPLAN symposium on principles and practice of parallel programming. 2020. (turingas)