Two Flavors, one giant leap: Tesla V100 PCI-E & Tesla V100 with NVLink

For those who love speeds and feeds, here’s a summary of the key enhancements vs Tesla P100 GPUs

	Tesla V100 with NVLink	Tesla V100 PCI-E	Tesla P100 with NVLink	Tesla P100 PCI-E	Ratio Tesla V100:P100
DP TFLOPS	7.8 TFLOPS	7.0 TFLOPS	5.3 TFLOPS	4.7 TFLOPS	~1.4-1.5X
SP TFLOPS	15.7 TFLOPS	14 TFLOPS	9.3 TFLOPS	8.74 TFLOPS	~1.4-1.5X
TensorFLOPS	125 TFLOPS	112 TFLOPS	21.2 TFLOPS 1/2 Precision	18.7 TFLOPS 1/2 Precision	~6X
Interface (bidirec. BW)	300GB/sec	32GB/sec	160GB/sec	32GB/sec	1.88X NVLink 9.38X PCI-E
Memory Bandwidth	900GB/sec	900GB/sec	720GB/sec	720GB/sec	1.25X
CUDA Cores (Tensor Cores)	5120 (640)	5120 (640)	3584	3584

Selecting the right Tesla V100 for you:

With Tesla P100 “Pascal” GPUs, there was a substantial price premium to the NVLink-enabled SXM2.0 form factor GPUs. We’re excited to see things even out for Tesla V100.

However, that doesn’t mean selecting a GPU is as simple as picking one that matches a system design. Here’s some guidance to help you evaluate your options:

Performance Summary

Increases in relative performance are widely workload dependent. But early testing demonstates HPC performance advancing approximately 50%, in just a 12 month period.

If you haven’t made the jump to Tesla P100 yet, Tesla V100 is an even more compelling proposition.

For Deep Learning, Tesla V100 delivers a massive leap in performance. This extends to training time, and it also brings unique advancements for inference as well.

Enhancements for Every Workload

Now that we’ve learned a bit about each GPU, let’s review the breakthrough advancements for Tesla V100.

Next Generation NVIDIA NVLink Technology (NVLink 2.0)

NVLink helps you to transcend the bottlenecks in PCI-Express based systems and dramatically improve the data flow to GPU accelerators.

The total data pipe for each Tesla V100 with NVLink GPUs is now 300GB/sec of bidirectional bandwidth—that’s nearly 10X the data flow of PCI-E x16 3.0 interfaces!

The NVLink Bank Account

NVIDIA NVLink technology continues to be a breakthrough for both HPC and Deep Learning workloads. But as with previous generations of NVLink, there’s a design choice related to a simple question:

Where do you want to spend your NVLink bandwidth?

Think about NVLink bricks as a “spending” or “bank account.” Each NVLink system-design strikes a different balance between where they “spend the funds.” You may wish to:

• Spend links on GPU:GPU communication
• Focus on increasing the number of GPUs
• Broaden CPU:GPU bandwidth to overcome the PCI-E bottleneck
• Create a balanced system, or prioritize design choices solely for a single workload

There are good reasons for each, or combinations, of these choices. DGX-1V, NumberSmasher 1U GPU Server with NVLink, and future IBM Power Systems products all set different priorities.

We’ll dive more deeply into these choices in a future post.

Net-net: the NVLink bandwidth increases from 160GB/sec to 300GB/sec (bidirectional), and a number of new, diverse HPC and AI hardware designs are now possible.

Programming Improvements

Tesla V100 and CUDA 9 bring a host of improvements for GPU programmers. These include:

• Cooperative Groups
• A new L1 cache + shared memory, that simplifies programming
• A new SIMT model, that relieves the need to program to fit 32 thread warps

We won’t explore these in detail in this post, but we encourage you to read the following resources for more:

• NVIDIA Volta Architecture Whitepaper
• Blog: Inside Volta
• GTC Presentation (PDF): Inside Volta

What does Tesla V100 mean for me?

Tesla V100 GPUs mean a massive change for many workloads. But each workload will see different impacts. Here’s a short summary of what we see:

1. An increase in performance on HPC applications (on paper FLOPS increase of 50%, diverse real-world impacts)
2. A massive leap for Deep Learning Training
3. 1 GPU, many Deep Learning workloads
4. New system designs, better tuned to your applications
5. Radical new, and radically simpler, programming paradigms (coherence)