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Found 15 papers, 5 papers with code
Fast Video Classification via Adaptive Cascading of Deep Models
Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining.
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
Learning to Optimize Tensor Programs
Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems.
Parameter Hub: a Rack-Scale Parameter Server for Distributed Deep Neural Network Training
Distributed deep neural network (DDNN) training constitutes an increasingly important workload that frequently runs in the cloud.
A Hardware-Software Blueprint for Flexible Deep Learning Specialization
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility.
ADARES: Adaptive Resource Management for Virtual Machines
Our large-scale analysis confirms that VMs are often misconfigured, either overprovisioned or underprovisioned, and that this problem is pervasive across a wide range of private clusters.
Scaling Distributed Machine Learning with In-Network Aggregation
Training machine learning models in parallel is an increasingly important workload.
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly
This mutual-training process between BO and the loss-prediction model allows us to limit the training steps invested in the BO search.
Cloud Collectives: Towards Cloud-aware Collectives forML Workloads with Rank Reordering
ML workloads are becoming increasingly popular in the cloud.
Efficient Direct-Connect Topologies for Collective Communications
Our algorithms start from small, optimal base topologies and associated communication schedules and use a set of techniques that can be iteratively applied to derive much larger topologies and schedules.
Bandwidth Optimal Pipeline Schedule for Collective Communication
We present a strongly polynomial-time algorithm to generate bandwidth optimal allgather/reduce-scatter on any network topology, with or without switches.
Symphony: Optimized DNN Model Serving using Deferred Batch Scheduling
However, existing model serving systems cannot achieve adequate batch sizes while meeting latency objectives as these systems eagerly dispatch requests to accelerators to minimize the accelerator idle time.
Punica: Multi-Tenant LoRA Serving
Our scheduler consolidates multi-tenant LoRA serving workloads in a shared GPU cluster.
Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
To maximize LLMs' serving throughput, we introduce Atom, a low-bit quantization method that achieves high throughput improvements with negligible accuracy loss.
ForestColl: Efficient Collective Communications on Heterogeneous Network Fabrics
ForestColl also outperforms other state-of-the-art schedule generation techniques with both up to 61\% more efficient generated schedules and orders of magnitude faster schedule generation speed.
