Presentation notes
Cuda developers x4 over all
Cuda developers in automotive and hyperscale x10
nV SDK:
Gameworks:
Volumetric lighting
Voxel Accelerated AO
Hybrid frustum traced shadows
Designworks:
Adobe MDL
Iray
Compute Works:
CUDA 8
cuDnn 5
nvGraph
Index plugin Visualization of data quickly
VRworks:
Oculus Rift and HTC Vive
Unreal, Max Play and Unity
Driveworks:
Still working on it, but is available to test with. Early access has already started Q1 of next year is the release.
nV Jetpack
GIE: GPU inference engine coming soon in May (jetson tx1: 24 images/watt), CUDA is the most energy efficient approach for deep learning.
VR:
Going to be able to do design visualization
Going to places where we can't normal go
Photo-real is a necessity, we need more performance.
Iray Vr can do this takes many GPU's and time to do this for photo realism VR but can be done in real time now.
Iray Vr lite, can be used on any hardware types and already has integration into 3dsMax and Maya and Google cardboard box coming in June.
AI:
5 years ago Deep learning started.
Alpha Go, 1000 cpu's and 60 GPU's. Computers powered by deep learning can do more than humans can program for.
New Computing model:
Deep learning Object detection, DNN, Data HPC
No longer have to have different programs written to do different things and it gets better results.
Industry funding is high 5 billion
AI has become a platform
P100 is in volume production.
P100 samples are out and they are being used by OEMs Q1 2017 servers will be available.
Deep learning supercomputer DGX-1
170 TF
3200 watts, 8 GPU's, 7 tb sdd's, etc.
12x faster performance for deep learning from last year.
Pascal with recurrent neuronets:
Interconnect is very important
Capabilities:
Persistent RNN's, keeping everything in the GPU with less
Register file for pascal 14mb vs 8 mb in Maxwell
nV link helps with splitting work across GPU's. Creates a wider model with more processors (30x more)
TensorFlow, DGX-1, easy adaptability, performance is key.
DGX-1 $129k
Already getting colleges and research labs and medicine are targeted
Cuda developers x4 over all
Cuda developers in automotive and hyperscale x10
nV SDK:
Gameworks:
Volumetric lighting
Voxel Accelerated AO
Hybrid frustum traced shadows
Designworks:
Adobe MDL
Iray
Compute Works:
CUDA 8
cuDnn 5
nvGraph
Index plugin Visualization of data quickly
VRworks:
Oculus Rift and HTC Vive
Unreal, Max Play and Unity
Driveworks:
Still working on it, but is available to test with. Early access has already started Q1 of next year is the release.
nV Jetpack
GIE: GPU inference engine coming soon in May (jetson tx1: 24 images/watt), CUDA is the most energy efficient approach for deep learning.
VR:
Going to be able to do design visualization
Going to places where we can't normal go
Photo-real is a necessity, we need more performance.
Iray Vr can do this takes many GPU's and time to do this for photo realism VR but can be done in real time now.
Iray Vr lite, can be used on any hardware types and already has integration into 3dsMax and Maya and Google cardboard box coming in June.
AI:
5 years ago Deep learning started.
Alpha Go, 1000 cpu's and 60 GPU's. Computers powered by deep learning can do more than humans can program for.
New Computing model:
Deep learning Object detection, DNN, Data HPC
No longer have to have different programs written to do different things and it gets better results.
Industry funding is high 5 billion
AI has become a platform
P100 is in volume production.
P100 samples are out and they are being used by OEMs Q1 2017 servers will be available.
Deep learning supercomputer DGX-1
170 TF
3200 watts, 8 GPU's, 7 tb sdd's, etc.
12x faster performance for deep learning from last year.
Pascal with recurrent neuronets:
Interconnect is very important
Capabilities:
Persistent RNN's, keeping everything in the GPU with less
Register file for pascal 14mb vs 8 mb in Maxwell
nV link helps with splitting work across GPU's. Creates a wider model with more processors (30x more)
TensorFlow, DGX-1, easy adaptability, performance is key.
DGX-1 $129k
Already getting colleges and research labs and medicine are targeted
Last edited:
My last Pascal guess wasn't too far off.
(Curiously they seem to have ditched the DLTOPs)
Edit: The announced GP100 has 3584 cores and based on the 1.48Ghz boost clock does 10.6 TF
So up from 3072 cores. Going from 8 to 15 B transistors only 512 more cores ? Most of the speedup comes from the higher clock. Additional going from 250W to 300W.
(Curiously they seem to have ditched the DLTOPs)
Edit: The announced GP100 has 3584 cores and based on the 1.48Ghz boost clock does 10.6 TF
So up from 3072 cores. Going from 8 to 15 B transistors only 512 more cores ? Most of the speedup comes from the higher clock. Additional going from 250W to 300W.
Last edited:
So GP100 has 3584 cores but:
"Each SM has 64 CUDA cores and four texture units, for a total of 3840 CUDA cores and 240 texture units."
Interestingly some left there. Also not nearly as big of a jump in shaders as I would've thought.
The amount of SMs has been doubled in GP100, it has 2x of registers and 1.5x of shared memory per lane
I guess the mid-grade GeForce SKU will use a third smaller chip than the P100, with GDDR5X. That would result in slightly beefier GPU than GM204, but with much better perf/Watt and TurboBoost range.
The multiprocessor design is very similar to Maxwell (sort of scaled down), now with more dedicated DP units and an updated ISA for mixed precision support.
The shared memory size has been reduced, though.
I hope the diagram isn't misleading considering dedicated DP units; I didn't expect otherwise to be honest but I also didn't expect as many
Well since all the software is going to be released in June and that software will need the next gen GPU to run..... something will be out by June.
Similar threads
- Locked
