NVIDIA Fermi Architecture

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Administrivia NVIDIA Fermi Architecture Patrick Cozzi University of Pennsylvania CIS 565 - Spring 2011 Assignment 4 grades returned Project checkpoint on Monday Post an update on your blog beforehand

1 Administrivia NVIDIA Fermi Architecture Patrick Cozzi University of Pennsylvania CIS Spring 2011 Assignment 4 grades returned Project checkpoint on Monday Post an update on your blog beforehand Poster session: 04/28 Three weeks from tomorrow G80, GT200, and Fermi November 2006: G80 June 2008: GT200 March 2011: Fermi (GF100) New GPU Generation What are the technical goals for a new GPU generation? 1

2 New GPU Generation What are the technical goals for a new GPU generation? Improve existing application performance. How? New GPU Generation What are the technical goals for a new GPU generation? Improve existing application performance. How? Advance programmability. In what ways? Fermi: What s More? More total cores (SPs) not SMs though More registers: 32K per SM More shared memory: up to 48K per SM More Super Functional Units (SFUs) Fermi: What s Faster? Faster double precision 8x over GT200 Faster atomic operations. What for? 5-20x Faster context switches Between applications 10x Between graphics and compute, e.g., OpenGL and CUDA 2

3 Fermi: What s New? G80, GT200, and Fermi L1 and L2 caches. For compute or graphics? Dual warp scheduling Concurrent kernel execution C++ support Full IEEE support in hardware Unified address space Error Correcting Code (ECC) memory support Fixed function tessellation for graphics G80, GT200, and Fermi GT200 and Fermi 3

Fermi Block Diagram GF100 16 SMs Each with 32 cores 512 total cores Each SM hosts up to 48 warps, or 1,536 threads

G80 8 cores GT200 8 cores GF100 32 cores Fermi SM Dual warp scheduling Why?

4 Fermi Block Diagram GF SMs Each with 32 cores 512 total cores Each SM hosts up to 48 warps, or 1,536 threads In flight, up to 24,576 threads Fermi SM Why 32 cores per SM instead of 8? Why not more SMs? G80 8 cores GT200 8 cores GF cores Fermi SM Dual warp scheduling Why? 32K registers 32 cores Floating point and integer unit per core 16 Load/stores 4 SFUs Fermi SM 16 SMs * 32 cores/sm = 512 floating point operations per cycle Why not in practice? 4

Fermi SM Each SM 64KB on-chip memory 48KB shared

shared memory Configurable by CUDA developer

5 Fermi SM Each SM 64KB on-chip memory 48KB shared memory / 16KB L1 cache, or 16KB L1 cache / 48 KB shared memory Configurable by CUDA developer Fermi Dual Warping Scheduling Fermi Caches Slide from: Slide from: 5

Fermi Caches Fermi: Unified Address Space Slide from: http://stanford-cs193g-sp2010.googlecode.

pdf Fermi: Unified Address Space 64-bit virtual addresses 40-bit physical addresses (currently) CUDA 4: Shared address space

6 Fermi Caches Fermi: Unified Address Space Slide from: Fermi: Unified Address Space 64-bit virtual addresses 40-bit physical addresses (currently) CUDA 4: Shared address space with CPU. Why? Fermi: Unified Address Space 64-bit virtual addresses 40-bit physical addresses (currently) CUDA 4: Shared address space with CPU. Why? No explicit CPU/GPU copies Direct GPU-GPU copies Direct I/O device to GPU copies 6

Fermi ECC Fermi Tessellation ECC Protected Register file, L1, L2, DRAM Uses

example, 64 bits is stored as 72 bits Fix single bit errors, detect multiple

Fermi Tessellation Fermi Tessellation Fixed function hardware on each SM for

7 Fermi ECC Fermi Tessellation ECC Protected Register file, L1, L2, DRAM Uses redundancy to ensure data integrity against cosmic rays flipping bits For example, 64 bits is stored as 72 bits Fix single bit errors, detect multiple bit errors What are the applications? Fermi Tessellation Fermi Tessellation Fixed function hardware on each SM for graphics Texture filtering Texture cache Tessellation Vertex Fetch / Attribute Setup Stream Output Viewport Transform. Why? 7

8 Observations Becoming easier to port CPU code to the GPU Recursion, fast atomics, L1/L2 caches, faster global memory In fact Observations Becoming easier to port CPU code to the GPU Recursion, fast atomics, L1/L2 caches, faster global memory In fact GPUs are starting to look like CPUs Beefier SMs, L1 and L2 caches, dual warp scheduling, double precision, fast atomics 8

CS GPU and GPGPU Programming Lecture 8+9: GPU Architecture 7+8. Markus Hadwiger, KAUST

CS GPU and GPGPU Programming Lecture 8+9: GPU Architecture 7+8. Markus Hadwiger, KAUST CS 380 - GPU and GPGPU Programming Lecture 8+9: GPU Architecture 7+8 Markus Hadwiger, KAUST Reading Assignment #5 (until March 12) Read (required): Programming Massively Parallel Processors book, Chapter