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Found 7 papers, 3 papers with code
TimeRL: Efficient Deep Reinforcement Learning with Polyhedral Dependence Graphs
We describe TimeRL, a system for executing dynamic DRL programs that combines the dynamism of eager execution with the whole-program optimizations and scheduling of graph-based execution.
ExclaveFL: Providing Transparency to Federated Learning using Exclaves
While current solutions have explored the use of trusted execution environment (TEEs) to combat such attacks, there is a mismatch with the security needs of FL: TEEs offer confidentiality guarantees, which are unnecessary for FL and make them vulnerable to side-channel attacks, and focus on coarse-grained attestation, which does not capture the execution of FL training.
Tenplex: Dynamic Parallelism for Deep Learning using Parallelizable Tensor Collections
After a GPU change, Scalai uses the PTC to transform the job state: the PTC repartitions the dataset state under data parallelism and exposes it to DL workers through a virtual file system; and the PTC obtains the model state as partitioned checkpoints and transforms them to reflect the new parallelization configuration.
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Quiver's key idea is to exploit workload metrics for predicting the irregular computation of GNN requests, and governing the use of GPUs for graph sampling and feature aggregation: (1) for graph sampling, Quiver calculates the probabilistic sampled graph size, a metric that predicts the degree of parallelism in graph sampling.
MSRL: Distributed Reinforcement Learning with Dataflow Fragments
Yet, current distributed RL systems tie the definition of RL algorithms to their distributed execution: they hard-code particular distribution strategies and only accelerate specific parts of the computation (e. g. policy network updates) on GPU workers.
CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers
Systems such as TensorFlow and Caffe2 train models with parallel synchronous stochastic gradient descent: they process a batch of training data at a time, partitioned across GPUs, and average the resulting partial gradients to obtain an updated global model.
Teechan: Payment Channels Using Trusted Execution Environments
We present Teechan, a full-duplex payment channel framework that exploits trusted execution environments.
Cryptography and Security
