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07/11/2023
PowerFusion: A Tensor Compiler with Explicit Data Movement Description and Instruction-level Graph IR
Deep neural networks (DNNs) are of critical use in different domains. To...
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01/18/2023
ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training
A key performance bottleneck when training graph neural network (GNN) mo...
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10/05/2022
GLM-130B: An Open Bilingual Pre-trained Model
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained lan...
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10/04/2022
Guiding the PLMs with Semantic Anchors as Intermediate Supervision: Towards Interpretable Semantic Parsing
The recent prevalence of pretrained language models (PLMs) has dramatica...
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08/02/2022
OLLIE: Derivation-based Tensor Program Optimizer
Boosting the runtime performance of deep neural networks (DNNs) is criti...
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05/24/2022
GraphQ IR: Unifying Semantic Parsing of Graph Query Language with Intermediate Representation
Subject to the semantic gap lying between natural and formal language, n...
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06/13/2021
G-TADOC: Enabling Efficient GPU-Based Text Analytics without Decompression
Text analytics directly on compression (TADOC) has proven to be a promis...
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03/24/2021
FastMoE: A Fast Mixture-of-Expert Training System
Mixture-of-Expert (MoE) presents a strong potential in enlarging the siz...
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09/23/2020
GraphPi: High Performance Graph Pattern Matching through Effective Redundancy Elimination
Graph pattern matching, which aims to discover structural patterns in gr...
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09/20/2020
TADOC: Text Analytics Directly on Compression
This article provides a comprehensive description of Text Analytics Dire...
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09/03/2020
ScalAna: Automating Scaling Loss Detection with Graph Analysis
Scaling a parallel program to modern supercomputers is challenging due t...
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08/17/2020
