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| NVIDIA Tesla K20X | |
|---|---|
| Name | NVIDIA Tesla K20X |
| Manufacturer | NVIDIA |
| Released | 2012 |
| Architecture | Kepler GK110 |
| Process | 28 nm |
| Cores | 2688 CUDA cores |
| Memory | 6 GB GDDR5 |
| Memory bus | 384-bit |
| Tdp | 235 W |
| Successor | NVIDIA Tesla K40 |
NVIDIA Tesla K20X
The NVIDIA Tesla K20X is a high-performance accelerator card introduced by NVIDIA as part of the Tesla product line aimed at scientific computing, high-performance computing, and enterprise data centers. Designed around the Kepler GK110 GPU, the K20X targeted supercomputers, research institutions, and national laboratories seeking accelerators for simulations, Los Alamos-scale projects, and weather-modelling efforts. Its release coincided with a broader industry shift toward heterogeneous computing exemplified by collaborations among Cray Inc., IBM, and accelerator vendors for exascale-oriented designs.
The K20X occupied a position between mainstream consumer graphics cards and dedicated HPC hardware used in installations like Oak Ridge National Laboratory clusters and Lawrence Livermore National Laboratory centers. It was part of competition among accelerators from AMD, producers of the Radeon Instinct line, and emerging efforts from startups seeking to challenge incumbent vendors. Institutions such as Argonne National Laboratory and universities running large-scale clusters evaluated the K20X for tasks ranging from computational chemistry referencing methods used by groups at MIT to astrophysics workflows common at Caltech and Stanford University.
Built on the GK110 die using a 28 nm fabrication process by TSMC, the K20X featured 2688 CUDA cores and relied on the Kepler streaming multiprocessor design that emphasized energy efficiency improvements over the preceding Fermi generation. The board shipped with 6 GB of GDDR5 memory on a 384-bit bus, delivering high memory bandwidth comparable to requirements in simulations run at Princeton University and University of California, Berkeley. The GPU incorporated double-precision floating point performance suited to scientific codes used by teams at NASA and the European Centre for Medium-Range Weather Forecasts. Hardware links such as PCI Express connected to host systems like those installed at Lawrence Berkeley National Laboratory.
In double-precision performance benchmarks, the K20X delivered significant gains over earlier Tesla models, showing improvements on workloads popular among researchers from Harvard University and University of Texas at Austin. Benchmarks from scientific suites—used by groups working on climate models at NOAA and molecular dynamics at Scripps Institution of Oceanography—demonstrated strong scaling for dense linear algebra libraries such as those developed at Argonne National Laboratory and Oak Ridge National Laboratory. Comparative studies pitted the K20X against accelerator offerings from AMD and accelerator clusters at European Organization for Nuclear Research for particle-physics simulations and lattice QCD calculations performed at CERN-affiliated centers.
The Tesla K20X followed the thermal and power constraints typical for enterprise accelerators, with a thermal design power around 235 W necessitating active cooling solutions integrated into chassis designs from vendors like Dell and Hewlett-Packard. Rack deployments in datacenters managed by organizations such as Fermilab and Riken used specialized heat sinks and airflow arrangements, while supercomputer cabinets from vendors like Cray Inc. adapted liquid-cooling and optimized airflow to support sustained workloads. The card’s full-height, dual-slot form factor fit in typical server ecosystems used by research centers including Los Alamos National Laboratory.
Software support centered on the CUDA ecosystem, with the K20X benefiting from toolchains developed by NVIDIA and community libraries maintained by institutions including Argonne National Laboratory and Oak Ridge National Laboratory. Developers at University of Illinois Urbana-Champaign and other research hubs used compilers, math libraries, and profiling tools—often coordinated with projects at Lawrence Livermore National Laboratory—to optimize kernels in domains like computational fluid dynamics and finite-element analysis. Popular frameworks such as versions of TensorFlow and domain-specific packages were ported to CUDA backends by research teams at Stanford University and University of Toronto to exploit the card’s parallelism.
The K20X was deployed in a variety of contexts: climate and weather forecasting groups at Met Office collaborators used it for ensemble simulations, seismic processing teams at Schlumberger-partnered centers leveraged its throughput for imaging algorithms, and computational chemistry groups at MIT used it for electronic-structure calculations. National laboratories and universities integrated K20X-equipped nodes into condensed-matter physics clusters at Los Alamos and cosmology pipelines at Caltech, while industry users in oil and gas and finance experimented with accelerating Monte Carlo workloads with K20X resources.
Upon release, reviewers from research consortia and media outlets contrasted the K20X’s scientific throughput with competing accelerators from AMD and noted its role in accelerating the adoption of GPU-accelerated HPC at institutions like Argonne National Laboratory and Oak Ridge National Laboratory. Its architecture influenced later NVIDIA products and informed software-porting efforts across the HPC community, contributing to the trajectory that led to subsequent generations used in exascale initiatives involving partners such as DOE laboratories and supercomputer vendors. The K20X remains cited in historical comparisons of accelerator performance alongside successors used in notable systems at Oak Ridge and CERN.
Category:Graphics processing units