GPU acceleration is the use of GPU as a supplementary component to the CPU to process large volumes of data. The CPU is the brain of any system, and it can handle multitasking and data processing by using one or more cores that handle data execution. The CPU is powerful enough to handle complex operations, but it struggles with high-volume processing; thus came the GPU. The GPU is also composed of cores for data execution, but it contains an enormous number of cores, although its cores are more simple and not as powerful as the CPU cores. Unlike the CPU, which relies on its computing power, GPUs rely on the number of cores to process data. While CPUs execute serial processing of data, GPUs are utilized for parallel processing, which makes them great for simple and repetitive calculations.
High-performance GPUs are leveraged on gaming and image rendering, which require the fast computation of a small set of equations. Two important concepts used in GPU acceleration are CPU overclocking and hardware acceleration. The CPU is not powerful enough to handle highly computational tasks, and it needs to offload high-volume computation to the GPU. This is where hardware acceleration comes in, where applications are configured for offloading tasks to the GPU. On the other hand, overclocking is the practice of pushing the CPU’s clock cycle beyond the manufacturer’s recommendation to improve its performance.
GPU-accelerated systems are usually found in data centers where large volumes of data are being processed. These systems require GPUs specifically designed to handle computationally intensive applications. As the prime maker of GPUs, Nvidia extended its arms to data center systems with Nvidia Tesla.
Science, research, engineering, and many other fields often require high computing for large volumes of data, but these were impossible in the previously available approaches. Nvidia paved the way for scientists and engineers to perform high-performance computing in their workstations with the power of Tesla GPUs.
Nvidia developed a parallel architecture for Tesla GPUs and designed Tesla products to meet HPC requirements. Nvidia Tesla features Thread Execution Manager and Parallel Data Cache. The former handles the execution of thousands of computing threads while the latter allows faster sharing of data and delivery of results. Nvidia Tesla GPUs optimize the productivity of data centers that heavily rely on high throughput.
Using Nvidia Tesla GPUs not only significantly improves the system’s performance but also helps reduce the operational cost of infrastructures by reducing the number of server nodes which consequently results in a reduction of budget for software and services. The operational cost is also significantly lower with Tesla products deployed since fewer equipment will need to be installed and greatly reduced power consumption.
Nvidia Tesla GPUs
Nvidia targets the high-performance computing market with the Tesla line of products. The first generation of Nvidia Tesla GPUs was released in May of 2007. These GPUs were based on the G80 chip and the company’s Tesla microarchitecture and used GDDR3 memory. The lower end C870 was an internal PCIe module with one G80 chip and 76.8 GB/s bandwidth. The mid-tier D870 had two G80 chips and twice the bandwidth of the C870 and was designed for deskside computers. The higher-end S870 was designed for computing servers with four G80 chips and four times the bandwidth of the C870.
Succeeding generations utilized Nvidia’s current microarchitecture at the time of their release and had higher bandwidth than the previous generation. The latest generation before the brand was retired was the Tesla V100 and T4 GPU Accelerator, which were released in 2018.
Tesla V100 is based on the Volta microarchitecture and uses the GV100 chip, which pairs CUDA cores with Tensor cores. The V100 is equipped with 5120 CUDA cores and 640 Tensor cores and delivers 125 teraFLOPS of deep learning performance. The V100 can replace hundreds of CPU-only servers and exceeds the requirements of HPC and deep learning. It is available in 32GB and 16GB configurations.
T4 GPU Accelerator is the only Turing-based Tesla GPU and was the last one to be released under the Tesla branding. The Tesla G4 GPU combines ray-tracing cores and Nvidia RTX technology for enhanced image rendering. It’s composed of 2560 CUDA cores and 320 Tensor cores and supports up to 16GB of GDDR6 memory. The T4 GPU is also power-efficient, using only 70 watts.
Brand Retirement and Rebranding
Tesla is not an uncommon name. Not only is it famous because of Nikola Tesla but also because of the popular brand of cars. To avoid confusion with the automobile brand, Nvidia decided to retire the Tesla branding for its GPU accelerators in 2019. Starting with the 2021 releases, Nvidia Tesla has been rebranded as Nvidia Data Center GPUs.
Tesla has garnered huge success in the data center industry, making the impossible possible with its superior performance and cost-efficient technology. Despite the rebranding, Nvidia instills Tesla’s characteristics in its GPU accelerators. The new generations are concurrent with Nvidia’s microarchitecture and use the latest chip and memory for better performance and higher bandwidth while keeping the power consumption low. Tesla has carved Nvidia’s name in data center systems, making Nvidia not only a trusted brand in gaming but also in the HPC market.