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NVIDIA Fermi Architecture
Patrick CozziUniversity of PennsylvaniaCIS 565 - Spring 2011
Administrivia
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)
Image from: http://stanford-cs193g-sp2010.googlecode.com/svn/trunk/lectures/lecture_11/the_fermi_architecture.pdf
New GPU Generation
What are the technical goals for a new GPU generation?
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 – 10xBetween graphics and compute, e.g.,
OpenGL and CUDA
Fermi: What’s New?
L1 and L2 caches. For compute or graphics?
Dual warp scheduling Concurrent kernel execution C++ support Full IEEE 754-2008 support in hardware Unified address space Error Correcting Code (ECC) memory support Fixed function tessellation for graphics
G80, GT200, and Fermi
Image from: http://stanford-cs193g-sp2010.googlecode.com/svn/trunk/lectures/lecture_11/the_fermi_architecture.pdf
G80, GT200, and Fermi
Image from: http://stanford-cs193g-sp2010.googlecode.com/svn/trunk/lectures/lecture_11/the_fermi_architecture.pdf
GT200 and Fermi
Image from: http://stanford-cs193g-sp2010.googlecode.com/svn/trunk/lectures/lecture_11/the_fermi_architecture.pdf
Fermi Block Diagram
Image from: http://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf
GF100 16 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 GF100 – 32 cores
Fermi SM
Image from: http://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf
Dual warp schedulingWhy?
32K registers 32 cores
Floating point and integer unit per core
16 Load/stores 4 SFUs
Fermi SM
Image from: http://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf
16 SMs * 32 cores/SM = 512 floating point operations per cycle
Why not in practice?
Fermi SM
Image from: http://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf
Each SM64KB 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
Image from: http://stanford-cs193g-sp2010.googlecode.com/svn/trunk/lectures/lecture_11/the_fermi_architecture.pdf
Slide from: http://gpgpu.org/wp/wp-content/uploads/2009/11/SC09_CUDA_luebke_Intro.pdf
Fermi Caches
Slide from: http://stanford-cs193g-sp2010.googlecode.com/svn/trunk/lectures/lecture_11/the_fermi_architecture.pdf
Fermi Caches
Slide from: http://stanford-cs193g-sp2010.googlecode.com/svn/trunk/lectures/lecture_11/the_fermi_architecture.pdf
Image from: http://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf
Fermi: Unified Address Space
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 copiesDirect GPU-GPU copiesDirect I/O device to GPU copies
Fermi ECC
ECC ProtectedRegister file, L1, L2, DRAM
Uses redundancy to ensure data integrity against cosmic rays flipping bitsFor example, 64 bits is stored as 72 bits
Fix single bit errors, detect multiple bit errors What are the applications?
Fermi Tessellation
Image from: http://stanford-cs193g-sp2010.googlecode.com/svn/trunk/lectures/lecture_11/the_fermi_architecture.pdf
Fermi Tessellation
Image from: http://stanford-cs193g-sp2010.googlecode.com/svn/trunk/lectures/lecture_11/the_fermi_architecture.pdf
Fermi Tessellation
Image from: http://stanford-cs193g-sp2010.googlecode.com/svn/trunk/lectures/lecture_11/the_fermi_architecture.pdf
Fixed function hardware on each SM for graphicsTexture filteringTexture cacheTessellationVertex Fetch / Attribute SetupStream OutputViewport Transform. Why?
Observations
Becoming easier to port CPU code to the GPURecursion, fast atomics, L1/L2 caches, faster
global memory In fact…
Observations
Becoming easier to port CPU code to the GPURecursion, 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
