Diffusion models have achieved great success in synthesizing high-quality images. However, generating high-resolution images with diffusion models is still challenging due to the enormous computational costs, resulting in a pro-hibitive latency for interactive applications. In this paper, we propose DistriFusion to tackle this problem by leveraging parallelism across multiple GPUs. Our method splits the model input into multiple patches and assigns each patch to a GPU. However,naïvely implementing such an algorithm breaks the interaction between patches and loses fidelity, while incorporating such an interaction will incur tremendous communication overhead. To overcome this dilemma, we observe the high similarity between the input from adjacent diffusion steps and propose displaced patch parallelism, which takes advantage of the sequential nature of the diffusion process by reusing the pre-computed feature maps from the previous timestep to provide context for the current step. Therefore, our method supports asynchronous commu-nication, which can be pipelined by computation. Extensive experiments show that our method can be applied to recent Stable Diffusion XL with no quality degradation and achieve up to a 6.1 ×speedup on eight A100 GPUs compared to one.

Traditional digital learning environments faced challenges in obtaining comprehensive user interaction data, often yielding fragmented insights without a cohesive visual representation. The emergence of metaverse platforms has enriched this landscape, enabling detailed user activity representation with multimodal data through avatars. However, how to understand the multimodal data related to teaching, social and cognitive presences underpinned by the 'Community of Inquiry' theoretical framework in the metaverse is a big challenge for educators. This study introduces a holistic visualisation solution to bridge this gap, ensuring a better understanding of avatars’ behaviours in an educational metaverse platform – Learningverse developed by our research team. The solution captures a range of multimodal data in Learningverse, such as avatar location, behaviours, emotions, and conversation. Key visualisation elements include heatmaps, points, and arrows, each with distinct informational value. In the future, integrating the solution with multimodal learning analytics is our next step work to understand teaching, social and cognitive presences.

In this poster, we reviewed 38 articles on learning analytics research, focusing on the data visualization interface designs. After examining the original ideas their interface design, a new visualization strategy was proposed to categorize and characterize them premised on their principles and approaches in four types, namely, (1) directly-presented, (2) outcome-oriented, (3) process-oriented, and (4) theory-oriented. Then, how these types of the visualization strategy could help facilitate learning analytics design and make data interpretable by users was presented.

Network reliability is critical for large clouds and online service providers like Microsoft. Our network is large, heterogeneous, complex and undergoes constant churns. In such an environment even small issues triggered by device failures, buggy device software, configuration errors, unproven management tools and unavoidable human errors can quickly cause large outages. A promising way to minimize such network outages is to proactively validate all network operations in a high-fidelity network emulator, before they are carried out in production. To this end, we present CrystalNet, a cloud-scale, high-fidelity network emulator. It runs real network device firmwares in a network of containers and virtual machines, loaded with production configurations. Network engineers can use the same management tools and methods to interact with the emulated network as they do with a production network. CrystalNet can handle heterogeneous device firmwares and can scale to emulate thousands of network devices in a matter of minutes. To reduce resource consumption, it carefully selects a boundary of emulations, while ensuring correctness of propagation of network changes. Microsoft's network engineers use CrystalNet on a daily basis to test planned network operations. Our experience shows that CrystalNet enables operators to detect many issues that could trigger significant outages.

Debugging faults in complex networks often requires capturing and analyzing traffic at the packet level. In this task, datacenter networks (DCNs) present unique challenges with their scale, traffic volume, and diversity of faults. To troubleshoot faults in a timely manner, DCN administrators must a) identify affected packets inside large volume of traffic; b) track them across multiple network components; c) analyze traffic traces for fault patterns; and d) test or confirm potential causes. To our knowledge, no tool today can achieve both the specificity and scale required for this task.

Clos-based networks including Fat-tree and VL2 are being built in data centers, but existing per-flow based routing causes low network utilization and long latency tail. In this paper, by studying the structural properties of Fat-tree and VL2, we propose a per-packet round-robin based routing algorithm called Digit-Reversal Bouncing (DRB). DRB achieves perfect packet interleaving. Our analysis and simulations show that, compared with random-based load-balancing algorithms, DRB results in smaller and bounded queues even when traffic load approaches 100%, and it uses smaller re-sequencing buffer for absorbing out-of-order packet arrivals. Our implementation demonstrates that our design can be readily implemented with commodity switches. Experiments on our testbed, a Fat-tree with 54 servers, confirm our analysis and simulations, and further show that our design handles network failures in 1-2 seconds and has the desirable graceful performance degradation property.

Reliable Group Data Delivery (RGDD) is a pervasive traffic pattern in data centers. In an RGDD group, a sender needs to reliably deliver a copy of data to all the receivers. Existing solutions either do not scale due to the large number of RGDD groups (e.g., IP multicast) or cannot efficiently use network bandwidth (e.g., end-host overlays). Motivated by recent advances on data center network topology designs (multiple edge-disjoint Steiner trees for RGDD) and innovations on network devices (practical in-network packet caching), we propose Datacast for RGDD. Datacast explores two design spaces: 1) Datacast uses multiple edge-disjoint Steiner trees for data delivery acceleration. 2) Datacast leverages in-network packet caching and introduces a simple soft-state based congestion control algorithm to address the scalability and efficiency issues of RGDD. Our analysis reveals that Datacast congestion control works well with small cache sizes (e.g., 125KB) and causes few duplicate data transmissions (e.g., 1.19%). Both simulations and experiments confirm our theoretical analysis. We also use experiments to compare the performance of Datacast and BitTorrent. In a BCube(4, 1) with 1Gbps links, we use both Datacast and BitTorrent to transmit 4GB data. The link stress of Datacast is 1.01, while it is 1.39 for BitTorrent. By using two Steiner trees, Datacast finishes the transmission in 16.9s, while BitTorrent uses 52s.

Reliable Group Data Delivery (RGDD) is a pervasive traffic pattern in data centers. In an RGDD group, a sender needs to reliably deliver a copy of data to all the receivers. Existing solutions either do not scale due to the large number of RGDD groups (e.g., IP multicast) or cannot efficiently use network bandwidth (e.g., end-host overlays).