Nvidia releases new GPU based on Ampere architecture

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By Julien Happich

The A100’s 40 GB (5-site) high-speed, HBM2 memory has a bandwidth of 1.6 TB/s, which is over 1.7x faster than V100. The 40 MB L2 cache on A100 is almost 7x larger than that of Tesla V100 and provides over 2x the L2 cache-read bandwidth, the company claims. Nvidia also released CUDA 11, the latest version of its Compute Unified Device Architecture parallel computing platform. CUDA 11 provides new specialized L2 cache management and residency control APIs on the A100. The SMs in A100 include a larger and faster combined L1 cache and shared memory unit (at 192 KB per SM) to provide 1.5x the aggregate capacity of the Volta V100 GPU.

The A100 GPU comes equipped with specialized hardware units including third-generation Tensor Cores, more video decoder (NVDEC) units, JPEG decoder and optical flow accelerators. All of these are used by various CUDA libraries to accelerate HPC and AI applications. The Multi-Instance GPU (MIG) feature can physically divide a single A100 GPU into multiple GPUs. It enables multiple clients such as VMs, containers, or processes to run simultaneously while providing error isolation and advanced quality of service (QoS) between these programs. MIG could be used to improve GPU utilization, for example to rent separate GPU instances, running multiple inference workloads on the GPU, hosting multiple Jupyter notebook sessions for model exploration, or resource sharing of the GPU among multiple internal users in an organization (single-tenant, multi-user).

MIG is transparent to CUDA and existing CUDA programs can run under MIG unchanged to minimize programming effort. For use in the enterprise datacenter, the Nvidia A100 introduces new memory error recovery features that improve resilience and avoid impacting running CUDA applications. Uncorrectable ECC errors on prior architectures would impact all running workloads on the GPU, requiring a reset of the GPU. On the A100, the impact is limited to the application that encountered the error and which is terminated, while other running CUDA workloads are unaffected. The GPU no longer requires a reset to recover. The NVIDIA driver performs dynamic page blacklisting to mark the page unusable so that current and new applications do not access the affected memory region.

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