LAB5GPGPU-sim Tutorial

Content

• Introduction

• Configurations of GPGPU-sim

• Experiment & Result

• References

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Introduction

What is GPU?

• Graphics Processing Unit

• Optimized for Highly Parallel Workloads

• Highly Programmable

• Commodity Hardware

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Architecture of V100

Why GPU?

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Architecture of GPU

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Software

• CUDA and OpenCL

• Extensions of C to support coprocessor model

• GPGPU-Sim support both

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What is GPGPU-sim?

• Microarchitecture performance model of contemporary GPUs• Functional model

• Timing model

• Power model: GPUWattch

• Runs unmodified CUDA/OpenCL

• BSD License

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Modules Overview

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CUDA/OpenCL

API Library

Interface

PTX Emulator

(CUDA-Sim)

GPGPU-Sim

Entrypoint

Timing

Model

Abstract

HW Model

Power

Model:

GPUWattch

The functional simulator

that executes PTX kernels

Top Level Organization

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Microarchitecture of GPU

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See details in the micro-2013 slides

GPGPU-sim: Function model(1/2)

• Functional model for PTX/SASS

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• Scalar PTX ISA(Instruction level)

• Scalar control flow (if-branch, for-loops)

• Register allocation not done in PTX

• Intermediate representation in CUDA tool chain

PTX(Parallel Thread eXecution)

• Better correlation with HW GPU

• NVIDIA’s cuobjdump

SASS (Native ISA for Nvidia GPUs)

GPGPU-sim: Function model(2/2)

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GPGPU-sim: Timing Model

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GPGPU-Sim simulates the timing model of a GPU running each launched CUDA kernel

• Reports # cycles spent running the kernels

• Exclude any time spent on data transfer on PCIe bus

• CPU may run concurrently with asynchronous

kernel launches.

GPGPU-Sim w/ SASS is ~0.98 correlated to the real HW

GPGPU-sim: Power Model

GPUWattch

• Estimate power consumed by the GPU according to the timing behavior

• Validated with power measurements from a real GTX 480

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Debugging and Visualization

GPGPU-Sim provides tools to debug and visualize simulated GPU behavior

• GDB macros• Cycle-level debugging

• AerialVision• High-level performance dynamics

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Configurations of GPGPU-sim

GPGPU-Sim ConfigurationsChange configuration by modifying ‘GTX480_run_dir/gpgpusim.conf’

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1. Simulation Run

2. Statistics Collection

3. High-Level Architecture

4. Additional Architecture

5. Scheduler

6. Shader Core Pipeline

7. Memory Sub-System

8. Operand Collector

9. DRAM/Memory Controller

10. Interconnection

11. PTX

12. Power information

Scheduler

Modify property of scheuler

• Number of warp scheuler in a core

• Issue number of warp scheuler

Examples • gpgpu_num_sched_per_core

• gpgpu_max_insn_issue_per_warp

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Shader Core Pipeline

Modify property of shader core

• Pipeline

• Register number

• Councurrent thread array

• Branch Divergence

Examples • gpgpu_shader_core_pipeline <# thread/shader core>:<warp size>:<pipeline SIMD width>

• gpgpu_shader_registers <# registers/shader core, default=8192>

• gpgpu_shader_cta <# CTA/shader core, default=8>

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Memory Sub-System Configuration

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Set up size and operation of serveral kinds of memory and cache

• Memory: share memory

• Cache: Texture, constant, instruction, data cache

Examples:• gpgpu_perfect_mem <0=off (default), 1=on>

• gpgpu_tex_cache:l1 <nsets>:<bsize>:<assoc>:<rep>:<wr>:<alloc>,<mshr>:<N>:<merge>,<mq>

• gpgpu_const_cache:l1 <nsets>:<bsize>:<assoc>:<rep>:<wr>:<alloc>,<mshr>:<N>:<merge>,<mq>

• gpgpu_cache:il1 <nsets>:<bsize>:<assoc>:<rep>:<wr>:<alloc>,<mshr>:<N>:<merge>,<mq>

• gpgpu_cache:dl2 <nsets>:<bsize>:<assoc>:<rep>:<wr>:<alloc>,<mshr>:<N>:<merge>,<mq>

• gpgpu_shmem_size <shared memory size, default=16kB>

• gpgpu_shmem_warp_parts

• gpgpu_flush_cache <0=off (default), 1=on>

Power information

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Simulate power model of GPGPU-Sim

Examples• power_simulation_enabled 1 # Enable power model

• gpuwattch_xml_file gpuwattch_gtx480.xml # choose the configuration file

• power_trace_enabled 1 # Enable output: detailed average power traces

• steady_power_levels_enabled 1 # Enable output: steady state average power levels and corresponding performance counters

Experiment & Result

Experiment Environment

• VirtualBox(Recommended)• Install Oracle VM VirtualBox

• Go to (http://www.gpgpu-sim.org/)

• Download fully setup virtual machine

• Double click this setup file

• Github• Go to (GPGPU-sim’s github)

• Follow the manual

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Installation - AerialVision

Step1. Install AerialVision dependencies$ sudo apt-get install python-pmw python-ply python-numpy libpng12-dev python-matplotlib

Step2. Run bin/aerialvision.py in GPGPU-Sim distribution$ python aerialvision.py

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HintVM Password: gpgpu-sim

Benchmarks

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63 cuda executable benchmarks

Run a simple program

CUDA program • Benchmark : vectorAdd.cu$ ./run_gpgpu-sim.sh ~/cuda/sdk/4.2/C/bin/linux/release/vectorAdd

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Host code

Device code

Simulation Result - Overall

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• simulation cycle of the GPU• simulation ins. of the GPU• IPC of the GPU• …

Overall report

Simulation result - Cache

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Behavior of every cache in the GPU• access times• misses times• pending hits times• reservation fails times

Cache report

Simulation result - Interconnect

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Statistics of the interconnect• Packet latency• Network latency…

Interconnect report

Simulation result - Power information

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Total power List of average power

Source code – You can modify it!

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Simulation result - Visualization

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Source code viewTime Lapse view

References

References

• GPGPU-sim Official Website

• GPGPU-sim Manual

• GPUWattch Manual

• Micro-45, Tutorial, GPGPU-Sim 3.x: A Performance Simulator for Manycore Accelerator Research

• McPAT

• NVIDIA OpenCL

• CUDA Toolkit

• Chinese Note

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