arXiv subjects

Om Amit Gandhi

Publications and source records attributed to Om Amit Gandhi.

3 recordsLinked to original sources

arXiv2026

Shards on a Shoestring: Empirical Characterization of NEAR Protocol Nightshade Sharding on Commodity Hardware

NEAR Protocol's Nightshade architecture targets one million transactions per second (TPS) through horizontal sharding of both state and computation. Published benchmarks were produced on expensive Google Cloud Platform infrastructure costing approximately \$700 per hour, leaving a significant reproducibility gap for academic research. We present the first independent empirical characterization of NEAR Nightshade sharding on commodity hardware: a Chameleon Cloud bare-metal node with 48 hyperthreaded Intel Xeon cores, 128\,GB RAM, and HDD storage at 80--100\,MB/s. We systematically sweep shard count from $N{=}1$ to $N{=}24$, measuring aggregate TPS, per-shard TPS, block time, BFT finality, memory, and disk I/O. We identify three distinct bottleneck regimes: L3 cache pressure at low $N$, witness gossip pipeline saturation at mid $N$, and coherence collapse at high $N$. A key unexpected finding is that HDD write latency acts as implicit flow control for the witness gossip pipeline. Removing it via RAM-backed tmpfs causes complete chain stall at $N{=}16$, with a 29$\times$ spike in orphan witness rate at 47\% CPU utilization. Aggregate TPS peaks at $N{=}8$ (+40\% over $N{=}1$) then reverses, with per-shard TPS collapsing 23$\times$ by $N{=}24$. Our dataset provides the first commodity-hardware calibration baseline for the companion SimPy sharding simulator.

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arXiv2026

VaultxGPU: GPU-Accelerated Blockchain Consensus

Blockchain consensus mechanisms based on Proof-of-Work consume significant energy, with Bitcoin alone estimated at approximately 150 TWh per year. Proof-of-Space reduces this cost by replacing repeated computation with storage, but plot generation remains bottlenecked by CPU hashing throughput. Prior work on VaultX demonstrated a high-performance CPU-based Proof-of-Space plotter using multi-threaded Blake3 hashing, achieving plotting speeds 4 to 50x faster than Chia depending on hardware configuration. In this paper, we present VaultxGPU, a GPU-accelerated extension of the VaultX plotter that offloads the Blake3 hashing pipeline to the GPU using custom kernels. We implement the plotter in both CUDA for NVIDIA hardware and SYCL for AMD and Intel GPUs, keeping Table 1 entirely in GPU VRAM and fusing the sort and match stages into a single kernel to minimize data movement. We evaluate VaultxGPU across K-values 27 through 31 against CPU baselines. Our SYCL GPU implementation achieves a 59.2x speedup over a single-threaded CPU baseline, completing a K=31 plot in 45.4 seconds compared to 2688 seconds, and outperforms even the best 384-thread CPU configuration. These results confirm that GPU acceleration is the correct direction for scaling Proof-of-Space plotting beyond what CPU parallelism can achieve.

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arXiv2026

Simulation-Based Performance Evaluation of Sharded Blockchain Architectures

Public blockchains continue to struggle with scalability because improving throughput is not as simple as increasing block size or reducing block interval. Larger blocks increase validation and transmission cost, while shorter intervals raise the likelihood of propagation delays, forks, and stale blocks. These limits motivate sharding, where transaction processing is divided across multiple parallel shard groups. In this work, we present a configurable SimPy-based discrete-event simulator for evaluating sharded blockchain architectures under controlled workload and network assumptions. The simulator models mining, verification, inter-shard coordination, block dissemination, measured throughput, average block time, and communication overhead. Our simulator achieves 1.6M TPS at 256 shards under a local datacenter-like setup and 0.6M TPS in a global WAN setup, showing strong throughput gains from parallel execution. However, the gains are not unbounded: beyond a certain number of shards, coordination traffic, synchronization, and network overhead begin to dominate, leading to diminishing returns.

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