Prism is the first symbolic superoptimizer for tensor programs that uses sGraph for compact representation of program families, two-level search, e-graph equivalence checking, and auto-tuning to achieve up to 2.2x speedup over prior superoptimizers on LLM workloads.
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cuDNN: Efficient primitives for deep learning
12 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels must be reoptimized, which makes maintaining codebases difficult over time. Similar issues have long been addressed in the HPC community by libraries such as the Basic Linear Algebra Subroutines (BLAS). However, there is no analogous library for deep learning. Without such a library, researchers implementing deep learning workloads on parallel processors must create and optimize their own implementations of the main computational kernels, and this work must be repeated as new parallel processors emerge. To address this problem, we have created a library similar in intent to BLAS, with optimized routines for deep learning workloads. Our implementation contains routines for GPUs, although similarly to the BLAS library, these routines could be implemented for other platforms. The library is easy to integrate into existing frameworks, and provides optimized performance and memory usage. For example, integrating cuDNN into Caffe, a popular framework for convolutional networks, improves performance by 36% on a standard model while also reducing memory consumption.
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PyTorch demonstrates compatibility of imperative Pythonic usability with high performance and accelerator support through its runtime architecture and benchmark results.
The Indirect Convolution algorithm avoids im2col by using an indirection buffer, reducing memory overhead proportionally to input channels and outperforming GEMM-based methods by up to 62% for convolutions requiring transformation.
ATLAAS automatically converts RTL-extracted bit-level accelerator semantics into tensor-level ISA specs via an 8-pass MLIR pipeline, enabling automated compiler backend generation for designs like Gemmini and VTA.
TileLoom compiles tile-based languages to spatial dataflow hardware by distributing tiles across cores and optimizing data reuse via on-chip networks, delivering vendor-comparable performance on Tenstorrent systems.
ShinkaEvolve improves sample efficiency in LLM-driven program evolution via parent sampling, code novelty rejection-sampling, and bandit LLM ensemble selection, achieving new SOTA circle packing with 150 samples and gains on math reasoning and competitive programming tasks.
A unified IR plus ML-based scheduling for CNN inference on multi-vendor integrated GPUs matches or exceeds vendor libraries (up to 1.62x) on image models while supporting more models.
CuLifter recovers types from untyped GPU register files via constraint propagation to lift 99.98% of 24,437 functions across 919 cubins to valid LLVM IR.
CuTile delivers high performance on select AI workloads and GPUs but varies significantly by architecture and is less portable than Triton across tested platforms.
TCL delivers 16.8x faster tuning on CPU and 12.48x on GPU with modestly lower inference latency by combining RDU active sampling, a lightweight Mamba cost model, and cross-platform continual knowledge distillation.
Warp-tiled CUDA kernel for depthwise convolution delivers 3.26x runtime reduction versus naive baseline and 1.29x end-to-end training speedup using counter-free analysis in cloud settings.
StableHLO serves as a viable unified representation for cross-architecture performance modeling of distributed ML workloads, preserving relative trends while exposing fidelity trade-offs.
citing papers explorer
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Prism: Symbolic Superoptimization of Tensor Programs
Prism is the first symbolic superoptimizer for tensor programs that uses sGraph for compact representation of program families, two-level search, e-graph equivalence checking, and auto-tuning to achieve up to 2.2x speedup over prior superoptimizers on LLM workloads.
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch demonstrates compatibility of imperative Pythonic usability with high performance and accelerator support through its runtime architecture and benchmark results.
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The Indirect Convolution Algorithm
The Indirect Convolution algorithm avoids im2col by using an indirection buffer, reducing memory overhead proportionally to input channels and outperforming GEMM-based methods by up to 62% for convolutions requiring transformation.
-
ATLAAS: Automatic Tensor-Level Abstraction of Accelerator Semantics
ATLAAS automatically converts RTL-extracted bit-level accelerator semantics into tensor-level ISA specs via an 8-pass MLIR pipeline, enabling automated compiler backend generation for designs like Gemmini and VTA.
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TileLoom: Automatic Dataflow Planning for Tile-Based Languages on Spatial Dataflow Accelerators
TileLoom compiles tile-based languages to spatial dataflow hardware by distributing tiles across cores and optimizing data reuse via on-chip networks, delivering vendor-comparable performance on Tenstorrent systems.
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ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution
ShinkaEvolve improves sample efficiency in LLM-driven program evolution via parent sampling, code novelty rejection-sampling, and bandit LLM ensemble selection, achieving new SOTA circle packing with 150 samples and gains on math reasoning and competitive programming tasks.
-
A Unified Optimization Approach for CNN Model Inference on Integrated GPUs
A unified IR plus ML-based scheduling for CNN inference on multi-vendor integrated GPUs matches or exceeds vendor libraries (up to 1.62x) on image models while supporting more models.
-
CuLifter: Lifting GPU Binaries to Typed IR
CuLifter recovers types from untyped GPU register files via constraint propagation to lift 99.98% of 24,437 functions across 919 cubins to valid LLVM IR.
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Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs
CuTile delivers high performance on select AI workloads and GPUs but varies significantly by architecture and is less portable than Triton across tested platforms.
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TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning
TCL delivers 16.8x faster tuning on CPU and 12.48x on GPU with modestly lower inference latency by combining RDU active sampling, a lightweight Mamba cost model, and cross-platform continual knowledge distillation.
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CUDA Kernel Optimization and Counter-Free Performance Analysis for Depthwise Convolution in Cloud Environments
Warp-tiled CUDA kernel for depthwise convolution delivers 3.26x runtime reduction versus naive baseline and 1.29x end-to-end training speedup using counter-free analysis in cloud settings.
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Evaluating Cross-Architecture Performance Modeling of Distributed ML Workloads Using StableHLO
StableHLO serves as a viable unified representation for cross-architecture performance modeling of distributed ML workloads, preserving relative trends while exposing fidelity trade-offs.