You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


---
abstract: 'MPI accomplishes portable, standardized message-passing between processes by exposing a standard API that hides the implementation of the underlying mechanism for message passing. Until now, checkpointing an MPI program required knowledge of these underlying mechanisms. Through the addition of a proxy, we demonstrate that MPI programs can be checkpointed and restarted regardless of the MPI implementation utilized. Further, proxies may enable MPI programs to be checkpointed on one MPI implementation, and restarted on another.'
author:
- Gregory Price
title: 'DMTCP Checkpoint/Restart of MPI Programs via Proxies'
---

Introduction
============

This work has largely been produced using examples from other DMTCP plugins from the core dmtcp library [@dmtcp]. It was implemented and tested against MPICH [@mpich], with examples from mpitutorial.com [@mpitut].

MPI provides the HPC community a standardized way to pass messages between processes in parallel computation. Each MPI implementation provides an opaque, standardized interface to send and receive messages; however, each implementation may differ in system requirements and mechanisms used to actually pass the message (i.e., sockets, pipes, shared memory, etc.).

This provides a challenge when checkpoint/restart is desired. Classically, checkpoint/restart is achieved via virtualization of various resources (such as file descriptors, sockets, shared memory, etc.), which are used transparently by the user application. Checkpoint/restart software must then provide support for each potential implementation of MPI; agnosticism is a pipe dream in this reality.

Through the use of MPI proxies and a DMTCP Plugin, we present a solution that provides simple, portable, transparent, and agnostic Checkpoint/Restart. Our implementation requires no changes to application code, and can be deployed on any architecture machine that supports DMTCP.

Our strategy makes one, key, novel observation about how to checkpoint MPI: don’t. By interposing on the MPI library, and moving all MPI procedures into a separate process, we only need to checkpoint a single, ephemeral interface between the application program and the proxy. Then, we simply, transparently translate MPI calls into calls to our MPI proxy.

Restart presents a separate problem, one which is fairly understood within the realm of checkpoint/restart: How do we handle messages in flight? There are two options for managing the situation.

1.  Log and replay of messages on restart

2.  Drain the network of in-flight messages, and cache them for restart

Logging many messages requires additional (potentially significant) overhead throughout the lifetime of the computation, while draining the network only incurs a cost at the time of checkpoint. We employ network draining as our mechanism of choice, under the assumption that a one-time cost during checkpoint is more tolerable to HPC professionals. This overhead can also be easily controlled through changing how often a checkpoint is created.

Finally, we believe solving this checkpoint/restart issue for MPI via the combination of library interposition (via DMTCP plugins) and a proxy provides a compelling case for using this strategy elsewhere. Other technologies that provide a standardized interface to an external service can also benefit from checkpoint/restart via the use of proxies.

Background
==========

MPI provides two main services:

1.  Creation of processes (called ranks) for distributed computation

2.  Message passing between these ranks

A coordinator is used for exchanging information about rank identification, how to build direct communication paths between ranks, and what groups have been created for publish-subscribe style communications (Broadcasting to a subset of ranks). It is useful to visualize this relationship in Figure \[fig:figure1\], as one possible implementation of an MPI coordinator.

![\[fig:figure1\]The communication structure of a simple MPI application](figure1.PNG){width="70.00000%"}

From here, we can see that there is a parent-child process relationship. The Coordinator is a parent to both Rank 0 and Rank 1. Also, there are at least three “communication interfaces” created between these three programs: Interfaces between each rank and the coordinator, and between each rank.

Each rank has a separate “MPI Library”, which is used by the application program to interact with the coordinator and other ranks. We call this an *Active MPI Library*. An “Active” library is one whose functions are loaded and executed, whereas a “passive” library is one whose functions may be referenced but never used.

![\[fig:figure3\]MPI library in User Application ](figure3.PNG){width="60.00000%"}

Prior work on checkpointing MPI programs had been built on the assumption that all external resources within the entire system must be virtualized. We can visualize this with a “checkpoint boundary” for an arbitrary MPI computation.

![\[fig:figure2\]The checkpoint boundary of existing solutions](figure2.PNG){width="75.00000%"}

Each relationship and communication mechanism within the checkpoint boundary will increase the complexity of a successful checkpoint/restart approach as the number of potential communication mechanisms used by MPI implementations increases. (The “checkpoint boundary” encapsulates all the resources that must be recorded and/or restored upon restart.)

Our Approach
============

We seek to avoid checkpointing any implementation-specific MPI code, and instead create a network with a checkpoint boundary that looks as follows:

![\[fig:figure4\]Our Checkpoint Boundary](figure4.PNG){width="75.00000%"}

This reduces the total number of interfaces needed to be virtualized, and completely segregates the MPI implementation from the user application. The MPI library used by the user application is no longer “active”, and is replaced by a DMTCP plugin that provides the virtual interface to the “active” library made available by the proxy.

challenges
==========

By introducing an external proxy, a one-step message-passing mechanism is broken into two steps. This introduces a temporal issue that was not present in other checkpointing solutions. We must deal with two challenges that must be solved in order to successfully checkpoint and restart:

1.  In-flight Data. Rank 0 sends Rank 1 a message, the message has been received by Proxy 0, but has not been transferred to Proxy 1 or Rank 1. A checkpoint is initiated, leaving the message stuck “in flight”.

2.  Proxy State. Rank 0 makes a call to receive a message, but Rank 1 has not sent the message yet. Rank 0 must re-issue the call in order to receive on restart.

In both cases, we recognize that a potential for failure occurs when the proxy has a “state” that is not being recorded due to our chosen checkpoint boundary. We revert to the two basic techniques discussed previously (Drain, and Log-and-Replay) to solve these in the simplest way possible.

For “in-flight data”, we drain the network of all in-flight messages, and cache this information in the receiving processes’ memory space. In order to know when all in-flight data has been successfully drained, we utilize the DMTCP coordinator to share the number of messages that each rank has sent and received. When draining, we continue to increment these numbers. When the numbers are equivalent, we know that there is no longer any data in flight, and thus we can move forward with checkpointing. This heuristic was previously used in the PhD thesis of Jiajun Cao for checkpointing over InfiniBand [@jiajun].

For Proxy-State replication, we break messages into two basic categories:

1.  Administrative messages

2.  Message actions

Administrative messages are messages between the rank and the MPI coordinator to either retrieve information about the current configuration of the MPI ranks, or to create new configurations (such as new groups of ranks). Configuration messages must be replayed to ensure that the “Active” MPI library in the proxy is in the same state as when we initiated the checkpoint.

Message actions are calls such as MPI\_IProbe and MPI\_Recv, where the user application is trying to retrieve either data, or information about the state of the network. For example, MPI\_IProbe is used to check if there is a message waiting to be received. The relevant information for these message actions must be cached at checkpoint time. The simplest example is the drain mechanism for in-flight data — these messages are cached for use on restart. A call to MPI\_Recv must check the cache for a buffered message before checking the proxy for a buffered message.

API Support
===========

Currently only the following calls to the MPI interface are fully supported.

-   MPI\_Init

-   MPI\_Finalize

-   MPI\_Comm\_size

-   MPI\_Comm\_rank

-   MPI\_Type\_size

-   MPI\_Send

-   MPI\_Recv

-   MPI\_Probe

-   MPI\_Iprobe

-   MPI\_Get\_count

Future work will expand support to the following API calls:

-   Non-blocking Asynchronous Send / Receive

    -   MPI\_Isend

    -   MPI\_Irecv

    -   MPI\_Test

-   Collective Communication

    -   MPI\_Bcast

    -   MPI\_Barrier

    -   MPI\_Scatter

    -   MPI\_Gather

    -   MPI\_Allgather

    -   MPI\_Reduce

    -   MPI\_Allreduce

-   Communicators and Groups

    -   MPI\_Comm\_group

    -   MPI\_Group\_incl

    -   MPI\_Comm\_create\_group

    -   MPI\_Group\_free

    -   MPI\_Comm\_free

Current State
=============

Presently we support checkpointing of simple Send/Receive programs that use any of the supported API calls. We do not currently support asynchronous send and receive (i.e., non-blocking sends and receives). Trivially, we support non-blocking receives by supporting MPI\_Iprobe, which allows user applications to determine if a message is waiting to be received. However, support of MPI\_Isend will require the caching of additional data (not yet implemented).

With these basic building blocks supported, we can confidently say that the remaining API calls can also be supported — it is now a simple matter of plumbing, and no longer a question of feasibility.

Future Work
===========

Along with adding full API support, we also intend to explore the idea of checkpointing an application on one MPI implementation, and then restarting the application on a different MPI implementation. This will be accomplished via two core mechanisms. First, we will provide a virtualized interface to MPI specific resources, such as an MPI\_Request id or MPI\_Status structure. Second, we will provide a new layer to handle translations between the *Passive MPI Library* in the MPI Plugin and the *Active MPI Library* in the MPI Proxy. We believe this approach will make it possible to, for example, checkpoint a program built and run on MPICH, and restart the same program on an OMPI implementation without recompilation.

Acknowledgements
================

Gene Cooperman and Rohan Garg (Northeastern University) provided guidance on DMTCP plugin creation, and in the design of the proxy architecture. The initial idea for the proxy architecture was developed by Rohan Garg, Gene Cooperman and Jiajun Cao [@jiajun] during the development of other checkpoint/restart technologies. This report and project reinforces their claim that a proxy architecture for checkpointing can be applicable much more generally.

The members of the High Performance Computing Lab at Northeastern University provided feedback and support during the development of this project.

[99]{}

DMTCP. http://github.com/dmtcp/dmtcp. MPICH. http://www.mpich.org. MPI Tutorials. http://mpitutorial.com Douglas Thain, Miron Livny: “Multiple Bypass: Interposition Agents for Distributed Computing. Cluster Computing” 4(1): 39-47 (2001) Jiajun Cao, Transparent Checkpointing over RDMA-based Networks, PhD thesis, 2017