Kepler Architecture

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What is GPU Computing? GPGPU, CUDA and Kepler Explained
About GPU Computing
Developing with CUDA
Kepler Architecture

KEPLER - THE WORLD'S FASTEST,
MOST EFFICIENT HPC ARCHITECTURE

Get 3x the performance with the NVIDIA® Kepler, the world's fastest and most efficient high performance computing (HPC) architecture. With innovative computing technology and features, it is applicable to a broader range of scientific computing applications and makes hybrid computing more accessible for application developers and researchers.



Kepler's break-through performance is made possible by:

Kepler SMX processing

SMX
Delivers more processing performance and efficiency through this new, innovative streaming multiprocessor design that allows a greater percentage of space to be applied to processing cores versus control logic
 
 
Dynamic Parallelism
Dynamic Parallelism
Simplifies GPU programming by allowing programmers to easily accelerate all parallel nested loops – resulting in a GPU dynamically spawning new threads on its own without going back to the CPU

 
Read the Dynamic Parallelism White Paper
 

Hyper-Q
Hyper-Q
Slashes CPU idle time by allowing multiple CPU cores to simultaneously utilize a single Kepler GPU, dramatically advancing programmability and efficiency
 
 
 

Learn more about NVIDIA Kepler

 
Kepler Compute Architecture Tech Brief


 
Kepler Compute Architecture White Paper

GPU Technology Conference 2012 Keynote: Kepler GPU Architecture.

 

Reference

Tesla GPU Computing Products
CUDA Parallel Computing Platform
Cloud Computing
Kepler Desktop GPUs for Gaming

Kepler SMX processing

Higher performance and efficiency achieved with SMX by increasing processing cores while reducing control logic.

Dynamic Parallelism

Dynamic Parallelism on Kepler GPU dynamically spawns new threads by adapting to the data without going back to the CPU, greatly simplifying GPU programming and accelerating a broader set of popular algorithms.

 

With Dynamic Parallelism, the grid resolution could be determined dynamically at runtime. The simulation can "zoom in" on areas of interest and avoid unnecessary calculation in areas with little change.

Hyper-Q

Kepler's Hyper-Q increases GPU utilization by providing streams access to 32 independent hardware work queues or MPI ranks leading to advanced programmability and efficiency.

 

Hyper-Q enables multiple CPU cores to launch work on a single GPU simultaneously, thereby dramatically increasing GPU utilization and slashing CPU idle times.