Post
1762
How many times have you said Pandas is slow and still kept on using it? ๐ผ๐จ
Get ready to say Pandas can be fast but it's expensive ๐
๐ Original Limitations:
๐ป CPU-Bound Processing: Traditional pandas operations are CPU-bound (mostly single-threaded๐ฐ), leading to slower processing of large datasets.
๐ง Memory Constraints: Handling large datasets in memory-intensive operations can lead to inefficiencies and limitations.
๐ฃ Achievements with @nvidia RAPIDS cuDF:
๐ GPU Acceleration: RAPIDS cuDF leverages GPU computing. Users switch to GPU-accelerated operations without modifying existing pandas code.
๐ Unified Workflows: Seamlessly integrates GPU and CPU operations, falling back to CPU when necessary.
๐ Optimized Performance: With extreme parallel operation opportunity of GPUs, this achieves up to 150x speedup in data processing, demonstrated through benchmarks like DuckDB.
๐ New Limitations:
๐ฎ GPU Availability: Requires a GPU (not everything should need a GPU)
๐ Library Compatibility: Currently in the initial stages, all the functionality cannot be ported
๐ข Data Transfer Overhead: Moving data between CPU and GPU can introduce latency if not managed efficiently. As some operations still run on the CPU.
๐ค User Adoption: We already had vectorization support in Pandas, people just didn't use it as it was difficult to implement. We already had DASK for parallelization. It's not that solutions didn't exist
Blog: https://developer.nvidia.com/blog/rapids-cudf-accelerates-pandas-nearly-150x-with-zero-code-changes/
For Jupyter Notebooks:
For python scripts:
Get ready to say Pandas can be fast but it's expensive ๐
๐ Original Limitations:
๐ป CPU-Bound Processing: Traditional pandas operations are CPU-bound (mostly single-threaded๐ฐ), leading to slower processing of large datasets.
๐ง Memory Constraints: Handling large datasets in memory-intensive operations can lead to inefficiencies and limitations.
๐ฃ Achievements with @nvidia RAPIDS cuDF:
๐ GPU Acceleration: RAPIDS cuDF leverages GPU computing. Users switch to GPU-accelerated operations without modifying existing pandas code.
๐ Unified Workflows: Seamlessly integrates GPU and CPU operations, falling back to CPU when necessary.
๐ Optimized Performance: With extreme parallel operation opportunity of GPUs, this achieves up to 150x speedup in data processing, demonstrated through benchmarks like DuckDB.
๐ New Limitations:
๐ฎ GPU Availability: Requires a GPU (not everything should need a GPU)
๐ Library Compatibility: Currently in the initial stages, all the functionality cannot be ported
๐ข Data Transfer Overhead: Moving data between CPU and GPU can introduce latency if not managed efficiently. As some operations still run on the CPU.
๐ค User Adoption: We already had vectorization support in Pandas, people just didn't use it as it was difficult to implement. We already had DASK for parallelization. It's not that solutions didn't exist
Blog: https://developer.nvidia.com/blog/rapids-cudf-accelerates-pandas-nearly-150x-with-zero-code-changes/
For Jupyter Notebooks:
%load_ext cudf.pandas
import pandas as pdFor python scripts:
python -m cudf.pandas script.py