Search Results for author:
Found 14 papers, 5 papers with code
IA2: Leveraging Instance-Aware Index Advisor with Reinforcement Learning for Diverse Workloads
This study introduces the Instance-Aware Index Advisor (IA2), a novel deep reinforcement learning (DRL)-based approach for optimizing index selection in databases facing large action spaces of potential candidates.
SIP: Autotuning GPU Native Schedules via Stochastic Instruction Perturbation
In this work, we explore the possibility of GPU native instruction optimization to further push the CUDA kernels to extreme performance.
X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation
Tensor graph superoptimisation systems perform a sequence of subgraph substitution to neural networks, to find the optimal computation graph structure.
MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Builder
Rewrite systems [6, 10, 12] have been widely employing equality saturation [9], which is an optimisation methodology that uses a saturated e-graph to represent all possible sequences of rewrite simultaneously, and then extracts the optimal one.
RLFlow: Optimising Neural Network Subgraph Transformation with World Models
Our proposed approach RLFlow can learn to perform neural network subgraph transformations, without the need for expertly designed heuristics to achieve a high level of performance.
Model-based Reinforcement Learning
reinforcement-learning
+1
BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters
Then it applies the expert-provided knowledge to the graph to further contextualize the system behavior.
GDDR: GNN-based Data-Driven Routing
We explore the feasibility of combining Graph Neural Network-based policy architectures with Deep Reinforcement Learning as an approach to problems in systems.
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB
The model is then incorporated in a standard Bayesian Optimization loop to find parameters that maximize RocksDB's IO throughput.
Learning Index Selection with Structured Action Spaces
Configuration spaces for computer systems can be challenging for traditional and automatic tuning strategies.
Wield: Systematic Reinforcement Learning With Progressive Randomization
Reinforcement learning frameworks have introduced abstractions to implement and execute algorithms at scale.
RLgraph: Modular Computation Graphs for Deep Reinforcement Learning
Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns.
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
In this work, we introduce LIFT, an end-to-end software stack for applying deep reinforcement learning to data management tasks.
Tuning the Scheduling of Distributed Stochastic Gradient Descent with Bayesian Optimization
We present an optimizer which uses Bayesian optimization to tune the system parameters of distributed stochastic gradient descent (SGD).
Learning Runtime Parameters in Computer Systems with Delayed Experience Injection
This paper investigates the use of deep reinforcement learning for runtime parameters of cloud databases under latency constraints.
