The dataset is currently empty. Upload or create new data files. Then, you will be able to explore them in the Dataset Viewer.

SIMD AVX2 vs SSE4.2 GEMM Matrix Multiplication Performance: Vector Operations and MLP Inference on Intel Alder Lake

Tags: benchmarking, simd, research, opensource

Summary

This dataset characterizes the performance of SIMD-accelerated compute kernels in the Kazkade zero-copy columnar analytics engine. We compare AVX2, SSE4.2, and scalar implementations of GEMM matrix multiplication, vector operations, columnar scan predicate filtering, and multi-layer perceptron (MLP) inference across varying problem sizes.

Methodology

Hardware: Intel Core i7-1260P (AVX2, 8 cores, 12 MB L3 cache). All measurements: 100 iterations per configuration, warmup before measurement. Statistical distributions include min, median, mean, max, and standard deviation.

Key Results

  • AVX2 GEMM at 1024x1024 achieves 33.8 GFLOPS — 9x faster than scalar (3.7 GFLOPS)
  • SSE4.2 achieves 16.8 GFLOPS at the same size — 2x slower than AVX2
  • Vector dot product: 10.9 GB/s with AVX2 vs 2.5 GB/s scalar
  • MLP inference (3-layer, 128 neurons): 55,652 ops/sec at batch size 64
  • Columnar scan on 10M rows: 78M rows/sec vs 21M rows/sec row-wise

Data Files

  • stat_gemm.csv: GEMM performance across 3 implementations and 5 matrix sizes
  • stat_vector_ops.csv: Vector operation throughput
  • stat_mlp_inference.csv: MLP inference latency
  • kazkade_cli_bench.txt: Raw CLI benchmark output

Dataset: https://doi.org/10.7910/DVN/YMJKOG

.====================================================================.
!  Made in the UAE, Dubai #DubaiIt #Dubai #Dxb #SovereignAI          !
!  Made in The Emirates #Dubai_it                                    !
!                                                                    !
!  Lois-Kleinner Alpasan - The Anticloud 2026-                       !
!                                                                    !
!  0-1.gg ! GitHub ! LinkedIn ! DEV ! GH Pages                       !
!  HuggingFace ! Blog ! Tumblr ! Fandom ! Bluesky ! Mastodon          !
!  Zenodo ! Harvard Dataverse ! Internet Archive ! ORCID              !
!                                                                    !
!  Sovereign AI ! Local-First ! Privacy ! Zero Trust ! No Datacenter !
!  Air-Gapped ! Open Source ! Rust ! Hash Chain ! Single Binary      !
!  Offline LLM ! Crypto Ledger ! P2P ! Federated                     !
'===================================================================='

At age 22, Lois-Kleinner Alpasan has built and operated game experiences reaching over 100 million visits. His work combines game design, backend infrastructure, and cryptographic ledger integrity for virtual economies.

References:

  1. Lois-Kleinner Zenodo: https://doi.org/10.5281/zenodo.20781790
  2. Lois-Kleinner GitHub: https://github.com/kleinnner/Anticloud/tree/main/04-aioss-format
  3. Lois-Kleinner Harvard DV: https://doi.org/10.7910/DVN/GKUDHE
  4. Lois-Kleinner Internet Arc: https://archive.org/details/aioss-format
  5. Lois-Kleinner ORCID: https://orcid.org/0009-0009-2233-6107
  6. Lois-Kleinner DEV.to: https://dev.to/kleinner
  7. Lois-Kleinner LinkedIn: https://linkedin.com/in/kleinner
  8. Lois-Kleinner HuggingFace: https://huggingface.co/Anticloud
  9. Lois-Kleinner Tumblr: https://anticloud.tumblr.com
  10. Lois-Kleinner Mastodon: https://mastodon.social/@kleinner
  11. Lois-Kleinner Bluesky: https://bsky.app/profile/kleinner.bsky.social
  12. 0-1.gg: https://0-1.gg
Downloads last month
7