Computer scientist Azim Afroozeh investigated how to redesign compression and storage methods so that data can be processed much faster while using less space.
Afroozeh’s research focused on designing a new generation of data storage formats that can keep up with modern computing hardware such as multi-core CPUs and GPUs. Today’s widely used formats, such as Parquet, were created in an earlier era and no longer fully exploit the capabilities of modern processors. This mismatch wastes computing power and slows down data analysis.
In his work, Afroozeh investigated how to redesign compression and storage methods so that data can be processed much faster while using less space. He explored questions such as: how do we reorganize data so it fits the parallel nature of today’s hardware? How can we compress data in ways that remain extremely fast to decode?The resulting work, FastLanes, proposes a fundamentally new file format built for the hardware of today and tomorrow.
He demonstrated that data files can be redesigned to be both much smaller and dramatically faster to read by aligning them with how modern hardware actually works. The key insight is that data should be stored in a layout that allows thousands of values to be processed in parallel, without bottlenecks.
The research shows that by reorganizing data and using new lightweight compression techniques, we can decode billions of values per second - often faster than reading uncompressed data. It also shows that these methods work not only on CPUs but also on GPUs, which are increasingly used in analytics and AI. In simple terms: computers can work far more efficiently when data is stored in the “language” that modern processors prefer. FastLanes proves that a file format designed with this principle in mind can outperform current systems by a wide margin.
Afroozeh combined theoretical analysis with extensive practical experimentation. First, he studied how real-world datasets behave and how modern processors - both CPUs and GPUs - handle data in parallel. Based on these insights, he designed new compression layouts and algorithms tailored to modern hardware. He then implemented all methods in high-performance C++ and evaluated them experimentally on many architectures, including Intel, AMD, Apple, Amazon Graviton, and NVIDIA GPUs. This allowed him to measure speed, storage savings, and integration into real query engines. Finally, he developed a complete prototype file format and validated it using real analytical workloads. All implementations were made open-source to ensure reproducibility and practical value.
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