The document discusses using RDMA (remote direct memory access) writes instead of reads to build a more efficient key-value store. It achieved 26 million key-value operations per second with an average latency of 5 microseconds. The method uses RDMA writes from clients to servers to send requests, and servers poll client memory and send replies via send, providing a 2x performance improvement over previous work. It was evaluated on systems with InfiniBand FDR and RoCE networking.
This document introduces tensors through examples. It defines a vector as a rank 1 tensor and a matrix as a rank 2 tensor. It then provides an example of a rank 3 tensor. The document discusses how to define an inner product between tensors and provides examples using vectors and matrices. It also gives an example of how derivatives of a function can produce tensors of different ranks. Finally, it introduces the concept of decomposing matrices into their symmetric and antisymmetric parts.
The document discusses using RDMA (remote direct memory access) writes instead of reads to build a more efficient key-value store. It achieved 26 million key-value operations per second with an average latency of 5 microseconds. The method uses RDMA writes from clients to servers to send requests, and servers poll client memory and send replies via send, providing a 2x performance improvement over previous work. It was evaluated on systems with InfiniBand FDR and RoCE networking.
This document introduces tensors through examples. It defines a vector as a rank 1 tensor and a matrix as a rank 2 tensor. It then provides an example of a rank 3 tensor. The document discusses how to define an inner product between tensors and provides examples using vectors and matrices. It also gives an example of how derivatives of a function can produce tensors of different ranks. Finally, it introduces the concept of decomposing matrices into their symmetric and antisymmetric parts.
This document outlines Chainer's development plans, including past releases from versions 1.0 to 1.5, apologies about installation complications, and new policies and release schedules going forward from version 1.6. Key points include making installation easier, adding backwards compatibility, releasing minor versions every 6 weeks and revision versions every 2 weeks, and potential future features like profiling, debugging tools, and isolating CuPy.
PFN Spring Internship Final Report: Autonomous Drive by Deep RLNaoto Yoshida
This is the final report for the spring internship 2016 at Preferred Networks. gym_torcs is released in mt github account: https://github.com/ugo-nama-kun/gym_torcs
This document summarizes an internship project using deep reinforcement learning to develop an agent that can automatically park a car simulator. The agent takes input from virtual cameras mounted on the car and uses a DQN network to learn which actions to take to reach a parking goal. Several agent configurations were tested, with the three-camera subjective view agent showing the most success after modifications to the reward function and task difficulty via curriculum learning. While the agent could sometimes learn to park, the learning was not always stable, indicating further refinement is needed to the deep RL approach for this automatic parking task.
This document summarizes a presentation on a network management service that uses a state management framework to monitor and control network state. The framework represents network state as key-value pairs and uses consensus algorithms like Paxos to synchronize state across data centers. It allows applications to propose and check state changes to ensure consistency and avoid conflicts when updating the network.
3. Network State
= スイッチ群の転送Rule,link帯域など
スイッチの動作(FIB Rule)
= Flow Matching + Forwarding
Flow(End-to-End)
= FIB Ruleのchain
Network Update
B
C
D
A
f1:
3
7/7
3/7
0/7
0/7
4/7
3
Match:Flow
f1
AcRon:Output
Port
A
f1:
4
11. Network State Model
ネットワークフロー的な一般的なモデル
• Network
State
Model
– Network
G
consists
of
• Switches
,
directed
Links
– Flow
f
consists
of
• Ingress
to
egress
switches
,
traffic
volume
• Forwarding
Model
– Tunnel
Based
– WCMP(Weighted
Cost
MulR
Path)
11
15. Network Update Scheduling
• ILPでも解けるけどfeasibleではない
• Theorem1.
In
the
presence
of
both
link
capacity
and
switch
memory
constraints
,
finding
a
feasible
update
schedule
is
NP-‐
Complete.
•
Theorem2.
In
the
presence
of
link
capacity
constraints
,
but
no
switch
memory
constraints
,
finding
the
fastest
update
schedule
is
NP-‐Complete
(証明は別論文)
15
16. Dionysus Scheduling
• Lemma
1.If
the
dependency
graph
is
DAG
,
finding
a
feasible
update
schedule
is
in
P.
• 簡単なDependency
GraphはDAG
• DAG上のクリティカルパス長で優先度付け
– CPLを値を各OpNodeで計算
16