![Simplified CUDA Programming Model
• Computation performed by a very large number of
independent small scalar threads (CUDA threads or
microthreads) grouped into thread blocks.
// C version of DAXPY loop.
void daxpy(int n, double a, double*x, double*y)
{ for (int i=0; i<n; i++)
y[i] = a*x[i] + y[i]; }
// CUDA version.
__host__ // Piece run on host processor.
int nblocks = (n+255)/256; // 256 CUDA threads/block
daxpy<<<nblocks,256>>>(n,2.0,x,y);
__device__ // Piece run on GP-GPU.
void daxpy(int n, double a, double*x, double*y)
{ int i blockIdx.x*blockDim.x + threadId.x;
if (i<n) y[i]=a*x[i=]+];y[i }
8](https://image.slidesharecdn.com/calecture13-190105115155/85/Graphics-processing-uni-computer-archiecture-8-320.jpg)
![Warps are multithreaded on core
• One warp of 32 µthreads is a
single thread in the hardware
• Multiple warp threads are
interleaved in execution on a
single core to hide latencies
(memory and functional unit)
• A single thread block can
contain multiple warps (up to
512 µT max in CUDA), all
mapped to single core
• Can have multiple blocks
executing on one core
15[Nvidia, 2010]](https://image.slidesharecdn.com/calecture13-190105115155/85/Graphics-processing-uni-computer-archiecture-15-320.jpg)
![GPU Memory Hierarchy
16
[ Nvidia, 2010]](https://image.slidesharecdn.com/calecture13-190105115155/85/Graphics-processing-uni-computer-archiecture-16-320.jpg)
![Nvidia Fermi GF100 GPU
18
[Nvidia,
2010]](https://image.slidesharecdn.com/calecture13-190105115155/85/Graphics-processing-uni-computer-archiecture-18-320.jpg)
![Apple A5X
Processor for
iPad v3 (2012)
• 12.90mm x 12.79mm
• 45nm technology
21[Source: Chipworks, 2012]](https://image.slidesharecdn.com/calecture13-190105115155/85/Graphics-processing-uni-computer-archiecture-21-320.jpg)
Graphics processing uni computer archiecture
- 1. Lecture 13: Graphics Processing Units (GPUs)
- 2. 2 Multimedia Extensions (aka SIMD extensions) • Very short vectors added to existing ISAs for microprocessors • Use existing 64-bit registers split into 2x32b or 4x16b or 8x8b – Lincoln Labs TX-2 from 1957 had 36b datapath split into 2x18b or 4x9b – Newer designs have wider registers » 128b for PowerPC Altivec, Intel SSE2/3/4 » 256b for Intel AVX • Single instruction operates on all elements within register 16b 16b 16b 16b 32b 32b 64b 8b 8b 8b 8b 8b 8b 8b 8b 16b 16b 16b 16b 16b 16b 16b 16b 16b 16b 16b 16b + + + +4x16b adds
- 3. Types of Parallelism • Instruction-Level Parallelism (ILP) – Execute independent instructions from one instruction stream in parallel (pipelining, superscalar, VLIW) • Thread-Level Parallelism (TLP) – Execute independent instruction streams in parallel (multithreading, multiple cores) • Data-Level Parallelism (DLP) – Execute multiple operations of the same type in parallel (vector/SIMD execution) • Which is easiest to program? • Which is most flexible form of parallelism? – i.e., can be used in more situations • Which is most efficient? – i.e., greatest tasks/second/area, lowest energy/task 3
- 4. Resurgence of DLP • Convergence of application demands and technology constraints drives architecture choice • New applications, such as graphics, machine vision, speech recognition, machine learning, etc. all require large numerical computations that are often trivially data parallel • SIMD-based architectures (vector-SIMD, subword- SIMD, SIMT/GPUs) are most efficient way to execute these algorithms 4
- 5. DLP important for conventional CPUs too • Prediction for x86 processors, from Hennessy & Patterson, 5th edition – Note: Educated guess, not Intel product plans! • TLP: 2+ cores / 2 years • DLP: 2x width / 4 years • DLP will account for more mainstream parallelism growth than TLP in next decade. – SIMD –single-instruction multiple-data (DLP) – MIMD- multiple-instruction multiple- data (TLP) 5
- 6. Graphics Processing Units (GPUs) • Original GPUs were dedicated fixed-function devices for generating 3D graphics (mid-late 1990s) including high-performance floating-point units – Provide workstation-like graphics for PCs – User could configure graphics pipeline, but not really program it • Over time, more programmability added (2001-2005) – E.g., New language Cg for writing small programs run on each vertex or each pixel, also Windows DirectX variants – Massively parallel (millions of vertices or pixels per frame) but very constrained programming model • Some users noticed they could do general-purpose computation by mapping input and output data to images, and computation to vertex and pixel shading computations – Incredibly difficult programming model as had to use graphics pipeline model for general computation 6
- 7. General-Purpose GPUs (GP-GPUs) • In 2006, Nvidia introduced GeForce 8800 GPU supporting a new programming language: CUDA – “Compute Unified Device Architecture” – Subsequently, broader industry pushing for OpenCL, a vendor-neutral version of same ideas. • Idea: Take advantage of GPU computational performance and memory bandwidth to accelerate some kernels for general-purpose computing • Attached processor model: Host CPU issues data- parallel kernels to GP-GPU for execution • This lecture has a simplified version of Nvidia CUDA-style model and only considers GPU execution for computational kernels, not graphics – Would probably need another course to describe graphics processing 7
- 8. Simplified CUDA Programming Model • Computation performed by a very large number of independent small scalar threads (CUDA threads or microthreads) grouped into thread blocks. // C version of DAXPY loop. void daxpy(int n, double a, double*x, double*y) { for (int i=0; i<n; i++) y[i] = a*x[i] + y[i]; } // CUDA version. __host__ // Piece run on host processor. int nblocks = (n+255)/256; // 256 CUDA threads/block daxpy<<<nblocks,256>>>(n,2.0,x,y); __device__ // Piece run on GP-GPU. void daxpy(int n, double a, double*x, double*y) { int i blockIdx.x*blockDim.x + threadId.x; if (i<n) y[i]=a*x[i=]+];y[i } 8
- 9. Programmer’s View of Execution 9 blockIdx 0 threadId 0 threadId 1 threadId 255 blockIdx 1 threadId 0 threadId 1 threadId 255 blockIdx (n+255/256) threadId 0 threadId 1 threadId 255 Create enough blocks to cover input vector (Nvidia calls this ensemble of blocks a Grid, can be 2-dimensional) Conditional (i<n) turns off unused threads in last block blockDim = 256 (programmer can choose)
- 10. GPU Hardware Execution Model • GPU is built from multiple parallel cores, each core contains a multithreaded SIMD processor with multiple lanes but with no scalar processor • CPU sends whole “grid” over to GPU, which distributes thread blocks among cores (each thread block executes on one core) – Programmer unaware of number of cores 10 Core 0 Lane 0 Lane 1 Lane 15 Core 1 Lane 0 Lane 1 Lane 15 Core 15 Lane 0 Lane 1 Lane 15 GPU Memory CPU CPU Memory
- 11. “Single Instruction, Multiple Thread” • GPUs use a SIMT model, where individual scalar instruction streams for each CUDA thread are grouped together for SIMD execution on hardware (Nvidia groups 32 CUDA threads into a warp) 11 µT0 µT1 µT2 µT3 µT4 µT5 µT6 µT7 ld x mul a ld y add st y Scalar instruction stream SIMD execution across warp
- 12. Implications of SIMT Model • All “vector” loads and stores are scatter-gather, as individual µthreads perform scalar loads and stores – GPU adds hardware to dynamically coalesce individual µthread loads and stores to mimic vector loads and stores • Every µthread has to perform stripmining calculations redundantly (“am I active?”) as there is no scalar processor equivalent 12
- 13. Conditionals in SIMT model • Simple if-then-else are compiled into predicated execution, equivalent to vector masking • More complex control flow compiled into branches • How to execute a vector of branches? 13 µT0 µT1 µT2 µT3 µT4 µT5 µT6 µT7 tid=threadid If (tid >= n) skip Call func1 add st y Scalar instruction stream SIMD execution across warp skip:
- 14. Branch divergence • Hardware tracks which µthreads take or don’t take branch • If all go the same way, then keep going in SIMD fashion • If not, create mask vector indicating taken/not-taken • Keep executing not-taken path under mask, push taken branch PC+mask onto a hardware stack and execute later • When can execution of µthreads in warp reconverge? 14
- 15. Warps are multithreaded on core • One warp of 32 µthreads is a single thread in the hardware • Multiple warp threads are interleaved in execution on a single core to hide latencies (memory and functional unit) • A single thread block can contain multiple warps (up to 512 µT max in CUDA), all mapped to single core • Can have multiple blocks executing on one core 15[Nvidia, 2010]
- 16. GPU Memory Hierarchy 16 [ Nvidia, 2010]
- 17. SIMT • Illusion of many independent threads • But for efficiency, programmer must try and keep µthreads aligned in a SIMD fashion – Try and do unit-stride loads and store so memory coalescing kicks in – Avoid branch divergence so most instruction slots execute useful work and are not masked off 17
- 18. Nvidia Fermi GF100 GPU 18 [Nvidia, 2010]
- 20. Fermi Dual-Issue Warp Scheduler 20
- 21. Apple A5X Processor for iPad v3 (2012) • 12.90mm x 12.79mm • 45nm technology 21[Source: Chipworks, 2012]
- 22. Historical Retrospective, Cray-2 (1985) • 243MHz ECL logic • 2GB DRAM main memory (128 banks of 16MB each) – Bank busy time 57 clocks! • Local memory of 128KB/core • 1 foreground + 4 background vector processors 22 Foreground CPU Shared Memory Core 0 Lane Local Memory Core 0 Lane Local Memory Core 0 Lane Local Memory Core 0 Lane Local Memory
- 23. GPU Future • High-end desktops have separate GPU chip, but trend towards integrating GPU on same die as CPU (already in laptops, tablets and smartphones) – Advantage is shared memory with CPU, no need to transfer data – Disadvantage is reduced memory bandwidth compared to dedicated smaller-capacity specialized memory system » Graphics DRAM (GDDR) versus regular DRAM (DDR3) • Will GP-GPU survive? Or will improvements in CPU DLP make GP-GPU redundant? – On same die, CPU and GPU should have same memory bandwidth – GPU might have more FLOPS as needed for graphics anyway 23