Zach Anderson
March 12, 2025 01:57
The Grace CPU processor of NVIDIA improves the effectiveness of ETL workloads, offering higher performance and energy savings compared to traditional X86 processors.
The CPU Grace CPU of NVIDIA establishes new standards in the field of workloads of extraction, transformation, load (ETL), offering unequaled energy performance and efficiency in data centers and cloud environments. According to NvidiaThe CPU Grace is equipped with high -performance V2 arm nuclei, a rapid evolutionary coherence fabric and a LPDDR5X memory with a large bandwidth at low power, making it an ideal choice to require data processing tasks.
Polar knot on cpu
Polars, an open source library for data processing, uses the power of the CPU Grace of Nvidia to considerably improve the workloads at node. Thanks to its Python API and its optimized Lazyframe operations, POLARS allows an effective data analysis, as shown in the PDS benchmark. In particular, CPU Grace has shown an acceleration of 25% compared to the fastest CPU X86, AMD Turin, with performance gains allocated to its default page size of 64K compared to the size of the smaller pages of X86.
The PDS reference, which consists in executing 22 analysis requests, underlined the higher performance and the energy efficiency of the CPU of the Grace processor. Energy consumption has been reduced by 65% compared to X86 servers, reflecting by an improvement of 2.7x performance per Watt and 1.6x of better performance per dollar.
Multinode Apache Spark on CPU
In a multinodian configuration, Apache Spark also benefits from the capabilities of the CPU Grace. The NVIDIA OpenSource NDS reference tools showed that an eight -node cluster using Grace processors almost corresponded to the performance of a Genoa AMD cluster while consuming much less energy. This efficiency allows the CPU cluster thanks to provide almost 40% additional performance at the same level of power.
Industry implications
The introduction of CPU Grace represents a significant change to more efficient and more effective data processing solutions. By optimizing ETL’s workloads, organizations can obtain more in -depth information while reducing operational costs. The high performance nuclei of the Grace architecture, the rapid fabric and the massive bandwidth of memory are particularly beneficial for operations with high data intensity.
The transition to architectures based on ARM as Nvidia Grace also opens the way to integrated CPU and GPU solutions, improving the capacities of AI and automatic learning applications. CPU compatibility with the ARM ecosystem further simplifies standardization between data centers.
Overall, the NVIDIA Grace processor not only promises improved ETL workload performance, but is also positioned as a lasting choice for future data center operations, offering substantial cost savings and environmental benefits.
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