Introduction to Graphics Hardware and GPU's

Home , Graphics hardware, Von Neumann architecture

Blockseminar:

Verteiltes Rechnen und Parallelprogrammierung

Tim Conrad

GPU Computer: CHINA.imp.fu-berlin.de

GPU Intro Today • Introduction to GPGPUs • “Hands on” to get you started • Assignment 3

• Projects

GPU Intro Traditional Computing

Von Neumann architecture: instructions are sent from memory to the CPU

Serial execution: Instructions are executed one after another on a single Central Processing Unit (CPU)

Problems: • More expensive to produce • More expensive to run • Bus speed limitation

GPU Intro Parallel Computing

Official-sounding definition: The simultaneous use of multiple compute resources to solve a computational problem. Benefits: • Economical – requires less power and cheaper to produce • Better performance – bus/bottleneck issue Limitations: • New architecture – Von Neumann is all we know! • New debugging difficulties – cache consistency issue

GPU Intro Flynn’s Taxonomy

Classification of computer architectures, proposed by Michael J. Flynn

• SISD – traditional serial architecture in computers. • SIMD – parallel computer. One instruction is executed many times with different data (think of a for loop indexing through an array) • MISD - Each processing unit operates on the data independently via independent instruction streams. Not really used in parallel • MIMD – Fully parallel and the most common form of parallel computing.

GPU Intro Enter CUDA

CUDA is NVIDIA’s general purpose parallel computing architecture • Designed for calculation-intensive computation on GPU hardware • CUDA is not a language, it is an API • We will look at the C implementation of CUDA • There exist wrapper for Java (jCUDA) and Python (PyCUDA)

GPU Intro What is GPGPU ?

• General Purpose computation using GPU in applications other than 3D graphics – GPU accelerates critical path of application • Data parallel algorithms leverage GPU attributes – Large data arrays, streaming throughput – Fine-grain SIMD parallelism – Low-latency floating point (FP) computation • Applications – see GPGPU.org – Game effects (FX) physics, image processing – Physical modeling, computational engineering, matrix algebra, convolution, correlation, sorting

GPU Intro Terms

• What is GPGPU? – General-Purpose computing on a Graphics Processing Unit – Using graphic hardware for non-graphic computations

• What is CUDA? – Compute Unified Device Architecture – Software architecture for managing data-parallel programming

GPU Intro CUDA Goals

• Scale code to hundreds of cores running thousands of threads • The task runs on the GPU independently from the CPU

GPU Intro Motivation

GPU Intro Graphic cards?

Logical Representation of Visual Information Output Signal

GPU Intro GPU Intro History of Graphics Hardware

• … - mid ’90s – SGI mainframes and workstations – PC: only 2D graphics hardware • mid ’90s – Consumer 3D graphics hardware (PC) • 3dfx, nVidia, Matrox, ATI, … – Triangle rasterization (only) – Cheap: pushed by game industry • 1999 – PC-card with TnL [ Transform and Lighting ] • nVIDIA GeForce: Graphics Processing Unit (GPU) – PC-card more powerful than specialized workstations • Modern graphics hardware – Graphics pipeline partly programmable – Leaders: ATI and nVidia • “ATI Radeon X1950” and “nVidia GeForce 8800” – Game consoles similar to GPUs (Xbox)

GPU Intro • Why are GPUs used for computing?

GPU Intro Motivation

• High CPU load – Physics, AI, sound, network, … • Graphics demand: – Fast memory access • Many lookups [ vertices, normal, textures, … ] – High bandwidth usage • A few GB/s needed in regular cases! – Large number of flops • Flops = Floating Point Operations [ ADD, MUL, SUB, … ] • Illustration: matrix-vector products (16 MUL + 12 ADD) x (#vertices + #normals) x fps = (28 Flops) x (6.000.000) x 30 ≈ 5GFlops

• Conclusion: Real time graphics needs supporting hardware!

GPU Intro CPU vs. GPU

• CPU – Fast caches – Branching adaptability – High performance • GPU – Multiple ALUs (arithmetic logic unit) – Fast onboard memory – High throughput on parallel tasks • Executes program on each fragment/vertex

• CPUs are great for task parallelism • GPUs are great for data parallelism

GPU Intro CPU vs. GPU - Hardware

• More transistors devoted to data processing

GPU Intro GPU Architecture

GPU Intro Processing Element

• Processing element = thread processor = ALU

GPU Intro Memory Architecture

• Constant Memory • Texture Memory • Device Memory

GPU Intro Data-parallel Programming

• Think of the CPU as a massively-threaded co-processor • Write “kernel” functions that execute on the device -- processing multiple data elements in parallel

• Keep it busy!  massive threading • Keep your data close!  local memory

GPU Intro Modern Graphics Hardware

• Features of nvidia Tesla C870 – Core speed 1.35 Ghz – 128 Cores (16x8 shader processors) – 51 GB/s memory bandwidth – 1.5 GB memory

GPU Intro Modern Graphics Hardware

• High Memory Bandwidth Graphics Card High bandwidth GPU 51GB/s Graphics memory Output 1.35Mhz 1.5GB

AGP bus Processor Chip 2GB/s High bandwidth AGP memory

Cache ½GB 77GB/s ½MB 3GB/s Parallel Processes Parallel CPU Main memory 3Ghz 2GB

GPU Intro • How to program a GPGPU?

GPU Intro CUDA Software Developer’s Kit (SDK)

GPU Intro NVCC Compiler’s Role: Partition Code and Compile for Device

mycode.cu Compiled by native Compiled by nvcc compiler: gcc, icc, cc compiler int main_data; __shared__ int sdata;

Main() { } int main_data; __host__ hfunc () { __shared__ sdata; int hdata; Main() {} <<>>(); Host Only __host__ hfunc () { __global__ gfunc() { } int hdata; int gdata; __global__ gfunc() { <<>> } int gdata; (); } } Interface

__device__ dfunc() { __device__ dfunc() { int ddata; int ddata; } } Device Only

GPU Intro CUDA Programming Model: A Highly Multithreaded Coprocessor

• The GPU is viewed as a compute device that: – Is a coprocessor to the CPU or host – Has its own DRAM (device memory) – Runs many threads in parallel • Data-parallel portions of an application are executed on the device as kernels which run in parallel on many threads • Differences between GPU and CPU threads – GPU threads are extremely lightweight • Very little creation overhead – GPU needs 1000s of threads for full efficiency • Multi-core CPU needs only a few

GPU Intro Thread Batching: Grids and Blocks

Host Device

Grid 1 • A kernel is executed as a grid Kernel Block Block Block of thread blocks 1 (0, 0) (1, 0) (2, 0)

– All threads share data memory Block Block Block space (0, 1) (1, 1) (2, 1) • A thread block is a batch of threads that can cooperate with Grid 2 Kernel each other by: 2 – Synchronizing their execution

• For hazard-free shared Block (1, 1) memory accesses – Efficiently sharing data through a Thread Thread Thread Thread Thread (0, 0) (1, 0) (2, 0) (3, 0) (4, 0) low latency shared memory Thread Thread Thread Thread Thread • Two threads from two different (0, 1) (1, 1) (2, 1) (3, 1) (4, 1)

blocks cannot cooperate Thread Thread Thread Thread Thread (0, 2) (1, 2) (2, 2) (3, 2) (4, 2)

GPU Intro Block and Thread IDs

• Threads and blocks have IDs Device – So each thread can decide Grid 1 what data to work on Block Block Block – Block ID: 1D or 2D (blockIdx.x, (0, 0) (1, 0) (2, 0) blockIdx.y) Block Block Block – Thread ID: 1D, 2D, or 3D (0, 1) (1, 1) (2, 1) (threadIdx.{x,y,z}) • Simplifies memory Block (1, 1)

addressing when processing Thread Thread Thread Thread Thread multidimensional data (0, 0) (1, 0) (2, 0) (3, 0) (4, 0) Thread Thread Thread Thread Thread – Image processing (0, 1) (1, 1) (2, 1) (3, 1) (4, 1)

– Solving PDEs on volumes Thread Thread Thread Thread Thread – … (0, 2) (1, 2) (2, 2) (3, 2) (4, 2)

GPU Intro Programming Model

Important Concepts: • Device: GPU, viewed as a co-processor. • Host: CPU • Kernel: data-parallel, computed-intensive positions of application running on the device.

GPU Intro Programming Model

Important Concepts: • Thread: basic execution unit • Thread block: A batch of thread. Threads in a block cooperate together, efficiently share data. Thread/block have unique id • Grid: A batch of thread block. that excuate same kernel. Threads in different block in the same grid cannot directly communicate with each other

GPU Intro Programming Model

Simple matrix multiplication example: cpu c program: cuda program:

GPU Intro Programming Model

Hardware implementation:

A set of SIMD Multiprocessors with On- Chip shared memory

See also:

http://www.nvidia.com/content /PDF/fermi_white_papers/NVIDI A_Fermi_Compute_Architecture_ Whitepaper.pdf

GPU Intro Programming Model

G80 Example: • 16 Multiprocessors, 128 Thread Processors • Up to 12,288 parallel threads active • Per-block shared memory accelerates processing.

GPU Intro Programming Model

Streaming Multiprocessor (SM) • Processing elements o 8 scalar thread processors o 32 GFLOPS peak at 1.35GHz o 8192 32-bit registers (32KB) o usual ops: float, int, branch... • Hardware multithreading o up to 8 blocks (3 active) residents at once o up to 768 active threads in total • 16KB on-chip memory • supports thread communication • shared amongst threads of a block

GPU Intro Programming Model

Execution Model:

GPU Intro Programming Model

Single Instruction Multiple Thread (SIMT) Execution:

• Groups of 32 threads formed into warps o always executing same instruction o share instruction fetch/dispatch o some become inactive when code path diverges o hardware automatically handles divergence • Warps are primitive unit of scheduling • pick 1 of 24 warps for each instruction slot. • all warps from all active blocks are time-sliced

GPU Intro • Memory Model

GPU Intro Memory Model

There are 6 Memory Types :

GPU Intro Memory Model

There are 6 Memory Types :

• Registers o on chip o fast access o per thread o limited amount o 32 bit

GPU Intro Memory Model

There are 6 Memory Types :

• Registers • Local Memory o in DRAM o slow o non-cached o per thread o relative large

GPU Intro Memory Model

There are 6 Memory Types :

• Registers • Local Memory • Shared Memory o on chip o fast access o per block o 16 KByte o synchronize between threads

GPU Intro Memory Model

There are 6 Memory Types :

• Registers • Local Memory • Shared Memory • Global Memory o in DRAM o slow o non-cached o per grid o communicate between grids

GPU Intro Memory Model

There are 6 Memory Types :

• Registers • Local Memory • Shared Memory • Global Memory • Constant Memory o in DRAM o cached o per grid o read-only

GPU Intro Memory Model

There are 6 Memory Types :

• Registers • Local Memory • Shared Memory • Global Memory • Constant Memory • Texture Memory o in DRAM o cached o per grid o read-only

GPU Intro CUDA Device Memory Space Overview

• Each thread can: (Device) Grid

– R/W per-thread registers Block (0, 0) Block (1, 0) – R/W per-thread local memory Shared Memory Shared Memory – R/W per-block shared memory Registers Registers Registers Registers – R/W per-grid global memory – Read only per-grid constant Thread (0, 0) Thread (1, 0) Thread (0, 0) Thread (1, 0) memory

– Read only per-grid texture Local Local Local Local memory Memory Memory Memory Memory

Host Global • The host can R/W Memory

global, constant, and Constant texture memories Memory Texture Memory GPU Intro Global, Constant, and Texture Memories (Long Latency Accesses)

• Global memory (Device) Grid – Main means of Block (0, 0) Block (1, 0) communicating R/W Data between host Shared Memory Shared Memory and device Registers Registers Registers Registers

– Contents visible to Thread (0, 0) Thread (1, 0) Thread (0, 0) Thread (1, 0) all threads

Local Local Local Local • Texture and Memory Memory Memory Memory Constant Memories Host Global – Constants initialized Memory Constant by host Memory

– Contents visible to Texture all threads Memory GPU Intro Courtesy: NDVIA Memory Model

• Registers • Shared Memory o on chip

• Local Memory • Global Memory • Constant Memory • Texture Memory o in Device Memory

GPU Intro Memory Model

• Global Memory • Constant Memory • Texture Memory o managed by host code o persistent across kernels

GPU Intro • CUDA API

GPU Intro CUDA Highlights: Easy and Lightweight

• The API is an extension to the ANSI C programming language Low learning curve

• The hardware is designed to enable lightweight runtime and driver High performance

GPU Intro To compute, we need to:

• Allocate memory that will be used for the computation (variable declaration and allocation) • Read the data that we will compute on (input) • Specify the computation that will be performed • Write to the appropriate device the results (output)

GPU Intro A GPU is a specialized computer

• We need to allocate space in the video card’s memory for the variables. • The video card does not have I/O devices, hence we need to copy the input data from the memory in the host computer into the memory in the video card, using the variable allocated in the previous step. • We need to specify code to execute. • Copy the results back to the memory in the host computer.

GPU Intro Initially:

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