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Found 10 papers, 5 papers with code
Optimal Kernel Orchestration for Tensor Programs with Korch
Prior approaches optimize kernel orchestration by greedily applying operator fusion, which fuses the computation of multiple operators into a single kernel, and miss a variety of optimization opportunities in kernel orchestration.
PowerFusion: A Tensor Compiler with Explicit Data Movement Description and Instruction-level Graph IR
To accelerate DNN computation, tensor compilers are proposed to generate efficient code on different domain-specific accelerators.
FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training
A key performance bottleneck when training graph neural network (GNN) models on large, real-world graphs is loading node features onto a GPU.
GLM-130B: An Open Bilingual Pre-trained Model
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.
Ranked #1 on
Language Modelling
on CLUE (OCNLI_50K)
Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing
The recent prevalence of pretrained language models (PLMs) has dramatically shifted the paradigm of semantic parsing, where the mapping from natural language utterances to structured logical forms is now formulated as a Seq2Seq task.
OLLIE: Derivation-based Tensor Program Optimizer
Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adoption in real-world tasks.
GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation
Subject to the huge semantic gap between natural and formal languages, neural semantic parsing is typically bottlenecked by its complexity of dealing with both input semantics and output syntax.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
FastMoE: A Fast Mixture-of-Expert Training System
However, training trillion-scale MoE requires algorithm and system co-design for a well-tuned high performance distributed training system.
AIPerf: Automated machine learning as an AI-HPC benchmark
The de facto HPC benchmark LINPACK can not reflect AI computing power and I/O performance without representative workload.
