Preface: OpenAI revealed that the project cost $100 million, took 100 days, and used 25,000 NVIDIA A100 GPUs. Each server equipped with these GPUs uses approximately 6.5 kW, so an estimated 50 GWh of energy is consumed during training.

Background: Parallel processing is a method in computing of running two or more processors (CPUs) to handle separate parts of an overall task. Breaking up different parts of a task among multiple processors will help reduce the amount of time to run a program. GPUs render images more quickly than a CPU because of its parallel processing architecture, which allows it to perform multiple calculations across streams of data simultaneously. The CPU is the brain of the operation, responsible for giving instructions to the rest of the system, including the GPU(s).

NVIDIA CUDA provides a simple C/C++ based interface. The CUDA compiler leverages parallelism built into the CUDA programming model as it compiles your program into code.
CUDA is a parallel computing platform and programming interface model created by Nvidia for the development of software which is used by parallel processors. It serves as an alternative to running simulations on traditional CPUs.

Vulnerability details:

CVE-2024-0110: NVIDIA CUDA Toolkit contains a vulnerability in command `cuobjdump` where a user may cause an out-of-bound write by passing in a malformed ELF file. A successful exploit of this vulnerability may lead to code execution or denial of service.

CWE‑787   Code execution, denial of service (Severity –  Medium)

Ref: The integer overflow may result in an out-of-bounds write.

Official announcement: Please refer to the vendor announcement for details – https://nvidia.custhelp.com/app/answers/detail/a_id/5564