GPU Concepts / Architectures

Kevin Stratford

kevin@epcc.ed.ac.uk

Material by: Alan Gray, Kevin Stratford

Outline

• Factors influencing performance
• How do CPU / GPU architectures differ?
• Some trends in architecture
• Programming scientific applications?

Factors influencing performance

CPUs: Clock speed

• Power consumed by a device $\propto fV^2$
• Historically, devices got faster by increasing clock frequency $f$
  • Voltage $V$ decreased to keep power managable
• Now, voltage cannot be decreased easily
  • Bits are represented as a voltage difference
  • Engineering constraints mean a finite voltage difference must be maintained for correct operation
• Increased clock frequency no longer available

See http://cpudb.stanford.edu/visualize/clock_frequency

CPUs: Memory latency

• Memory latency is a serious concern in design
  • Can cost $O(100)$ clock cycles to retrieve data from main memory
  • Steps often taken in hardware to reduce latencies
• Cache hierarchies
  • Large on-chip caches to stage data
  • L1, L2, L3, ..., controllers, coherency mechanisms, ...
• Other latency-hiding measures
  • Hardware multithreading ("hyperthreading")
  • Out-of-order execution

E.g., Intel Sandybridge die

Image: http://www.theregister.co.uk/ (Source: Intel)

CPUs: Memory bandwidth

• CPUs generally use commodity DDR RAM
  • "Double Data Rate"
  • Manufacturers often don't publish bandwidth data
  • A standard benchmark might give $O(100)$ GB / s
• In practice, memory bandwidth can be important
  • Many real applications bandwidth limited

CPUs: Parallelism

• Source of increased performance now to use more cores
• Almost all commodity processors are now "many-core" or "multi-core"
• Limited clock speed keeps power consumption per core managable

E.g., Intel Knights Landing die (72 cores)

Image: http://www.hardwareluxx.de/ (Source: Intel)

CPUs: Summary

• CPUs: a highly successful design for general purpose computing
  • Used for operating systems, data bases, input/output, ...
  • Inevitable design compromises...
• ...mean not specifically intended for HPC
  • Much functionality, e.g., branch prediction, may not be required
  • Hardware / power devoted to infrequent (HPC) operations
• Huge commercial market
  • Huge investment involved in fabrication of new chips
  • Bespoke HPC chips not economically viable

... from the dismal science...

GPUs

• Large lucrative market in another area: games
  • Require Graphical Processing Units
  • Two dominant manufacturers: AMD and NVIDIA
• GPUs are designed to do rendering
  • An embarrassingly parallel problem
  • Favours a balance between floating point/memory bandwidth
• How is design reflected in factors influencing performance?

GPUs: Clock speed

• Underlying clock frequency relatively modest
  • Perhaps in high 100s MHz
• Performance based on parallelism...

GPUs: Memory latency

• Problem has not gone away
  • Strategy to hide it again related to parallelism
• Schedule very many independent tasks
  • More than can be accommodated on hardware at one time
• If one task encounters a long-latency operation
  • ... swapped out and schedule another task
  • Done in hardware, so fast
  • Multithreading "writ large"

GPUs: Memory Bandwidth

Source: NVIDIA

Parallelism: Basic unit is streaming multiprocessor (SM)

Source: https://devblogs.nvidia.com/parallelforall/inside-pascal/

Many SMs form a graphics processing cluster (GPC)

Source: https://devblogs.nvidia.com/parallelforall/inside-pascal/

Trends in Architecture

• Certain convergence in architectures
  • CPUs / GPUs will probably become more integrated
  • Memory space may become more uniform
• No doubt in need for parallelism
  • More cores / threads

Programming

• Graphics processing languages were / are used
  • DirectX, OpenGL
  • One or two early "heroic efforts" in scientific applications
• In 2007 NVIDIA developed CUDA
  • Compute Unified Device Architecture
  • Primarily to make graphics programming easier
  • As a by-product, scientific applications become more tractable

Programming

• At the same time, OpenCL was developed
  • Important in mobile phones, games
  • Not so much traction in scientific applications
• Directives-based approaches are available
  • Standards situation was relatively slow to become clear
  • Relatively pain-free
  • Sources of poor performance can be obscured

Big Data / Machine Learning

Source: https://xkcd.com/1897/

Scientific Applications

• What are GPUs good for...?
  • Problems with many independent tasks
  • Problems with significant (data-) parallelism
  • Favours structured code with identifable kernels
• ...and not so good for?
  • Highly coupled problems with little parallelism
  • IO-dominated problems
  • Poorly structured core with diffuse computational intensity
• Some growth areas
  • Reduced (half-, quarter-) precision arithmetic
  • Work based on task graphs

Some examples

• Linear algebra
  • BLAS (e.g., cuBLAS)
  • Petsc (CUDA/OpenCL)
  • Trilinos (Kokkos?)
• Quantum chemistry / Molecular dynamics
  • VASP (no GPU implementation)
  • CP2K (some CUDA)
  • Gromacs (some CUDA), LAMMPS (Kokkos)
• Lattice based / CFD
  • Fluent (CUDA)
  • OpenFOAM (none)

Summary

• GPUs offer the opportunity of cost and energy efficient computing
• The holy grail for programming:
  performance, portability, productivity