This thesis presents a GPU-accelerated string compression algorithm based on FSST (Fast Static Symbol Table).
The proposed compressor leverages several advanced CUDA techniques to optimize performance, including a voting mechanism that maximizes memory bandwidth and an efficient gathering pipeline utilizing stream compaction.
Additionally, the algorithm uses GPU compute capacity to support a memory-efficient encoding table through a space-time tradeoff.

The compression task is parallelized by tiling input data and adapting the data layout.
We introduce multiple compression pipelines, each with distinct tradeoffs.
To maximize encoding kernel throughput, the design introduces sliding windows and output packing to optimize register use and maximize effective memory bandwidth.
Pipeline-level throughput is further enhanced by introducing pipelined transposition stages and stream compaction to remove intermediate padding efficiently.

We evaluate these pipelines across several benchmark datasets and compare the best-performing version against state-of-the-art GPU compression algorithms, including nvCOMP, GPULZ, and compressors generated using the LC framework.
The proposed compressor achieves a throughput of 74GB/s on an RTX4090 while maintaining compression ratios comparable to FSST.
In terms of compression ratio, it consistently outperforms ANS, Bitcomp, Cascaded, and GPULZ across all datasets.
Its overall throughput exceeds that of GPULZ and all nvCOMP compressors except ANS, Bitcomp, Cascaded, and those produced by the LC framework.
Our compressor lies on the Pareto frontier for all evaluated datasets, advancing the state-of-the-art toward ideal compression.
It achieves near-identical compression ratios to FSST (except for machine-readable datasets), while achieving a speedup of 42.06x.
Compared to multithreaded CPU compression, it achieves a 6.45x speedup.

To assess end-to-end performance, we integrate the compressor with the GSST decompressor. The resulting (de)compression pipeline achieves a combined throughput of 55GB/s, outperforming uncompressed data transfer on links with a bandwidth up to 37.5 GB/s.
It also outperforms all state-of-the-art (de)compressors when the link bandwidth ranges between 3GB/s and 20GB/s.

While further research is needed to enhance robustness and integrate the compressor into analytical engines, this work demonstrates a viable and Pareto-optimal alternative to existing string compression methods.

The source code of all our compression pipelines is publicly available on GitHub.
This work also serves as the foundation for a scientific paper that has been accepted for presentation at ADMS 2025.

In this paper, we consider the Reliable Communication and Byzantine Reliable Broadcast problems on partially connected networks with authenticated links. We consider the Reliable Communication (RC) problem on partially connected networks, and the Byzantine Reliable Broadcast (BRB) problem on partially and fully connected networks. Danny Dolev's protocol works on the former, while Gabriel Bracha's authenticated double echo protocol works on the latter in the case of a fully connected network. By layering the two protocols the BRB problem can be solved for partially connected networks. The state-of-the-art protocols for these problems focus on unknown topologies, whereas we focus on known topologies. We show that these protocols can be optimized when processes leverage this knowledge. Our simulations with our profiler show that we can drastically reduce the message complexity and network usage (e.g., a reduction of 71.9% and 79.4% respectively with a 12B payload when N=150 and f=20 for Dolev) compared to naive routing with our optimizations and disjoint path solver.