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Found 9 papers, 5 papers with code
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years.
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
In this work, we present DiffPack, a torsional diffusion model that learns the joint distribution of side-chain torsional angles, the only degrees of freedom in side-chain packing, by diffusing and denoising on the torsional space.
Accelerating Barnes-Hut t-SNE Algorithm by Efficient Parallelization on Multi-Core CPUs
t-SNE remains one of the most popular embedding techniques for visualizing high-dimensional data.
DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling
DistGNN-MB trains GraphSAGE and GAT 10x and 17. 2x faster, respectively, as compute nodes scale from 2 to 32.
Efficient and Generic 1D Dilated Convolution Layer for Deep Learning
Finally, we demonstrate the performance of our optimized 1D convolution layer by utilizing it in the end-to-end neural network training with real genomics datasets and achieve up to 6. 86x speedup over the oneDNN library-based implementation on Cascade Lake CPUs.
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible.
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads
The TPP specification is platform-agnostic, thus code expressed via TPPs is portable, whereas the TPP implementation is highly-optimized and platform-specific.
Deep Graph Library Optimizations for Intel(R) x86 Architecture
The Deep Graph Library (DGL) was designed as a tool to enable structure learning from graphs, by supporting a core abstraction for graphs, including the popular Graph Neural Networks (GNN).
LISA: Towards Learned DNA Sequence Search
Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics.
