NVIDIA Tesla P100 : Specifications, Architecture, Working, Differences & Its Applications

The NVIDIA Tesla P100 was the first GPU launched at the GTC (GPU Technology Conference) by NVIDIA CEO, “Jen-Hsun Huang” in April 2016. Initially, it was released as a high-end accelerator for servers; subsequently, it was expanded to include a PCIe version for enhanced compatibility with large servers. It was the first GPU to feature HBM2 (High Bandwidth Memory 2), incorporated onto the identical package with an extensive 4096-bit memory bus. This article elaborates on the NVIDIA Tesla P100 GPU, its working, and its applications.

What is NVIDIA Tesla P100?

The NVIDIA Tesla P100 is an HPC (high-performance computing) GPU, built on the Pascal architecture. This GPU is designed for professional workloads and data centers, which include scientific simulations, big data analytics, AI training, etc. It features CUDA cores -3,584, HBM2 memory – up to 16GB, and up to 720 GB/s high-speed memory bandwidth by providing very strong performance within both FP32 and FP64 floating point calculations.

Its architecture & features were developed to speed up HPC and deep learning applications. This GPU uses a PCIe interface and a passive cooling solution to make it appropriate for server environments that need significant processing power for HPC and AI tasks.

NVIDIA Tesla P100 Specifications

The specifications of the NVIDIA Tesla P100 GPU include the following.

• This GPU uses Pascal Architecture.
• It includes 3,584 CUDA cores.
• Memory is 16GB HBM2.
• Memory Interface is 4096-bit.
• Memory Bandwidth is up to 732 GB/s.
• Single-Precision or FP32 is 9.3 TFLOPS.
• Double-Precision or FP64 is 4.7 TFLOPS.
• Half-Precision or FP16 is 18.7 TFLOPS.
• Boost Clock is Up to 1480 MHz.
• Interface is PCIe Gen3 x16.
• Max Power Consumption or TDP is 250 W.
• Form Factor is Dual-slot.
• The thermal solution is passive.

How does the NVIDIA Tesla P100 GPU Work?

The NVIDIA Tesla P100 data center GPU accelerator works by boosting performance for deep learning and high-performance computing workloads. It uses a combination of CUDA cores, a high-bandwidth HBM2 memory system & the NVLink interconnect mainly for multi-GPU scaling. In addition, it supports a variety of precision levels like FP32, FP16, and FP64. This GPU is designed for servers within data centers to manage tasks like scientific simulations, big data analytics, and AI model training more efficiently as compared to traditional CPUs.

The NVIDIA Tesla P100 GPU works on the Pascal architecture with huge parallelism and numerous technological innovations to speed up HPC & deep learning workloads. Therefore, it works by offloading different compute-intensive tasks and processing them with a highly parallel structure from the CPU.

NVIDIA Tesla P100 Architecture

The NVIDIA Tesla P100 uses the NVIDIA Pascal architecture based on a 16nm FinFET process. This was the initial GPU to utilize this Pascal architecture. It was designed to deliver a significant performance jump over earlier generations. It uses technologies like HBM2 memory with CoWoS (Chip-on-Wafer-on-Substrate) packaging, NVLink interconnect & a focus on high FP16 performance, especially for AI workloads.

Architecture Components

The key architectural components of NVIDIA Tesla P100 GPU include: streaming multiprocessor, shared memory, L2 cache, HBM2 memory, CUDA cores, unified memory and page migration engine, GPUDirect, NVlink interconnect, ECC memory, etc.

Streaming Multiprocessor

The Streaming Multiprocessor or SM is the primary, extremely parallel processing unit within the Pascal architecture. It works similarly to a CPU core, but is designed for huge parallelism. This GPU contains 60 of these SMs on the GP100 chip. The Pascal SM architecture has a simpler data path with a unified cache that combines L1 & texture cache capabilities.

Shared Memory

The NVIDIA Tesla P100 GPU supports shared memory for each thread block up to 96KB. Thus, this is a fast and on-chip memory, shared between all threads in a single block. It allows high-speed data sharing & cooperation among threads to optimize performance. It provides a major speed benefit over global memory and is essential for data coalescing techniques. So it involves including threads that read & write global memory within a unit-stride pattern.

L2 Cache

The NVIDIA Tesla P100 features 4096 KB of L2 cache, which is a hardware-managed, on-chip cache shared across all SMs to accelerate data access from the major memory. This cache reduces latency by frequently storing used data. It works as a unified and large cache that holds data copies from the main memory, shared by all SMs within the GPU.

L2 cache’s main role is to enhance performance by decreasing the number of trips to the slower global memory, which reduces memory traffic & latency. The L2 cache is automatically managed by the hardware, so developers do not require assigning or accessing it manually.

HBM2 Memory

The NVIDIA Tesla P100 uses HBM2 memory that is stacked on the Chip-on-Wafer-on-Substrate (CoWoS) package. It is available in both 12GB and 16GB versions, which provide up to 720 GB/s bandwidth through a 4096-bit memory interface. So it allows high performance for AI and scientific workloads. Therefore, this HBM2 implementation was a main factor in the performance as compared to earlier generations.

CUDA Cores

The NVIDIA GP100 GPU features fundamental processing units like 3584 CUDA cores. It performs parallel computations by providing high performance for deep learning, big data analytics, AI workloads, and scientific simulations. These cores are arranged into SMs (Streaming Multiprocessors), which are responsible for performing the parallel program instructions written with CUDA.

Unified Memory & Page Migration Engine

The Unified Memory in this architecture is a single and system-wide virtual address space, which is accessible by all CPUs & GPUs. This memory delivers a single and shared address space. It is accessible by both the GPU and CPU, which enhances performance by decreasing data movement. Thus, it allows applications to manage larger datasets as compared to GPU memory. The Page Migration Engine is a dedicated hardware unit in the GPU. It supports hardware page faulting & migration.

GPUDirect

GPUDirect is a technology that allows GPUs to exchange data directly with each other & also with other devices over the network without going through the CPU. This technology allows for a direct data path between a third-party peer device and GPU memory. Therefore, this significantly enhances performance by decreasing data transfer times & overhead, particularly for high-bandwidth applications. The P100 GPU supports GPUDirect RDMA by allowing direct memory access between the network devices and the GPU.

NVLink Interconnect

It is a high-speed, bidirectional, and high-bandwidth interconnect that allows 5x higher performance as compared to PCIe. Therefore, it extensively improves performance for multi-GPU and mixed-CPU/GPU workloads as compared to usual PCIe connections. The NVLink in this architecture provides higher bidirectional bandwidth for quick data transmission. It is essential for accelerating demanding applications like high-performance computing and deep learning.

ECC Memory

This memory in this architecture ensures data reliability and integrity. It is significant for data centers and high-performance computing environments. It detects & corrects single-bit memory errors & flags multi-bit errors automatically by avoiding data corruption within applications wherever data accuracy is supreme. In addition, it supports this feature natively, without capacity or performance overhead.

NVIDIA Tesla P100 Software

This GPU’s software features GPU drivers for a variety of operating systems, like Windows and Linux. These are available from Dell or HPE server manufacturers. In addition, it can also support CUDA & OpenCL parallel computing frameworks for deep learning and scientific workloads. Users will require locating the particular driver for their OS & server hardware. It may also require updating the video BIOS of the GPUs for complete functionality. To know the difference between NVDIA Tesla P100 TO NVDIA Tesla V100  read on NVDIA V100 Architecture

NVIDIA GPU T4 Vs NVIDIA GPU P100

The difference between NVIDIA GPU T4 and NVIDIA GPU P100 includes the following.

How to Maintain NVIDIA Tesla P100?

The steps involved in NVIDIA Tesla P100 are: ensuring correct environmental conditions, regular physical cleaning & maintaining the firmware & drivers updated. In addition, Tesla P100 is designed only for specific server environments, but not for typical workstations. Thus, it needs robust, flow-through cooling.

• This is a passively cooled graphics card, designed to work in a server chassis that delivers specific and high-airflow cooling.
• Make sure the server fans are working properly & the system is not overheating.
• Monitoring tools must be used to verify temperatures & manage fan speeds.
• Physical cleaning is frequently mandatory because dust buildup can hinder fans and heatsinks, which can lead to thermal problems.
• Keeping the software and drivers updated to ensure bug fixes, security updates, and stability.
• The P100 needs a suitable system BIOS &sufficient PCIe lanes for best function.

Advantages

The advantages of the NVIDIA Tesla P100 GPU include the following.

• The NVIDIA Tesla P100 GPU provides significant benefits like exceptional performance & efficiency within the data center.
• It delivers significant raw computing power, like peak double-precision – 5.3 teraflops, peak single-precision performance – 10.6 teraflops.
• The P100 GPU has native hardware support for FP16 arithmetic that provides over 21 teraflops of peak performance, particularly for deep learning workloads.
• It is built on the 16nm FinFET fabrication process that provides outstanding performance for each watt, which helps data centers handle power & cooling costs.
• A single P100 GPU node replaces various commodity CPU nodes, which enhances overall data center throughput & potentially saves up to 70% in the whole data center costs.
• The Tesla platform can support over 450 GPU-optimized HPC applications across various fields like quantum chemistry, climate modeling, and molecular dynamics.
• It features Error Correcting Code (ECC) protection for improved reliability and data integrity, which is essential for data center and demanding HPC environments.
• It is available in both NVLink and PCIe-enabled server configurations.
• This GPU is designed for different data center needs like hyperscale & mixed-workload HPC environments.

Disadvantages

The disadvantages of the NVIDIA Tesla P100 GPU include the following.

• Its drawbacks are: high power requirements, lack of active cooling, and a design like a data center accelerator instead of a consumer product.
• It uses the older Pascal architecture, which lacks the main features of current GPUs. Thus, it makes it extensively less efficient for current deep learning and AI workloads.
• Modern libraries & software tools for ML and AI are increasingly removing support for Pascal architecture, making it difficult to run current applications without custom patches or building from source.
• This GPU provides very little performance compared to the latest GPUs.
• This GPU does not contain any display connections as a data center product and is not proposed for gaming or graphics output.
• Its cost is high for their present performance output, particularly when compared to the latest consumer-grade cards.
• This card utilizes a passive heat sink and depends on a high-airflow server chassis for cooling. It cannot be used in a standard consumer desktop PC case without significant modifications & risks overheating within unsuitable environments.
• It has a maximum 250W of power draw that needs a robust power supply unit & suitable power connectors.
• Its specific cooling, BIOS, and power requirements may cause compatibility problems with normal consumer motherboards & systems. Thus, it leads to detection or boot loop problems.

Applications

The applications of the NVIDIA Tesla P100 GPU include the following.

• The NVIDIA Tesla P100 GPU is used for artificial intelligence, big data analytics, cloud computing, and high-performance computing applications.
• It is used in powering data centers for scientific simulations, cloud gaming, and AI training and inference by providing significant computational power, mainly for complex workloads.
• This P100 GPU accelerates AI training & inference by allowing complex tasks like medical image analysis for predicting illnesses & developing self-driving car technology.
• It is a core component of current HPC data centers, used for a broad range of scientific & engineering simulations like materials science, cosmology, and climate modeling.
• The P100 GPU is used to design powerful cloud servers for virtual desktop infrastructure & cloud gaming. Therefore, it allows users to stream high-performance applications to low-power devices.
• Scientific researchers can use it to speed up demanding applications within various fields like seismology, computational finance, molecular dynamics simulations, etc.
• Its processing power is used for analyzing huge datasets to extract insights.

In summary, the NVIDIA Tesla P100 GPU was a groundbreaking professional accelerator, designed particularly for deep learning, high-performance computing, and scientific simulation workloads in data centers. Therefore, it is a high-performance, data center GPU accelerator that uses the Pascal architecture. Here is a question for you: What is the NVIDIA Tesla V100 GPU?