MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems

Sparse linear algebra kernels play a critical role in numerous applications, covering from exascale scientific simulation to large-scale data analytics. Offloading linear algebra kernels on one GPU will no longer be viable in these applications, simply because the rapidly growing data volume may exceed the memory capacity and computing power of a single GPU. Multi-GPU systems nowadays being ubiquitous in supercomputers and data-centers present great potentials in scaling up large sparse linear algebra kernels. In this work, we design a novel sparse matrix representation framework for multi-GPU systems called MSREP, to scale sparse linear algebra operations based on our augmented sparse matrix formats in a balanced pattern. Different from dense operations, sparsity significantly intensifies the difficulty of distributing the computation workload among multiple GPUs in a balanced manner. We enhance three mainstream sparse data formats -- CSR, CSC, and COO, to enable fine-grained data distribution. We take sparse matrix-vector multiplication (SpMV) as an example to demonstrate the efficiency of our MSREP framework. In addition, MSREP can be easily extended to support other sparse linear algebra kernels based on the three fundamental formats (i.e., CSR, CSC and COO)

New-Sum: A Novel Online ABFT Scheme for General Iterative Methods

Emerging high-performance computing platforms, with large component counts and lower power margins, are anticipated to be more susceptible to soft errors in both logic circuits and memory subsystems. We present an online algorithm-based fault tolerance (ABFT) approach to efficiently detect and recover soft errors for general iterative methods. We design a novel checksum-based encoding scheme for matrix-vector multiplication that is resilient to both arithmetic and memory errors. Our design decouples the checksum updating process from the actual computation, and allows adaptive checksum overhead control. Building on this new encoding mechanism, we propose two online ABFT designs that can effectively recover from errors when combined with a checkpoint/rollback scheme. These designs are capable of addressing scenarios under different error rates. Our ABFT approaches apply to a wide range of iterative solvers that primarily rely on matrix-vector multiplication and vector linear operations. We evaluate our designs through comprehensive analytical and empirical analysis. Experimental evaluation on the Stampede supercomputer demonstrates the low performance overheads incurred by our two ABFT schemes for preconditioned CG (0:4% and 2:2%) and preconditioned BiCGSTAB (1:0% and 4:0%) for the largest SPD matrix from UFL Sparse Matrix Collection. The evaluation also demonstrates the exibility and effectiveness of our proposed designs for detecting and recovering various types of soft errors in general iterative methods

RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model

Text-to-image generation (TTI) refers to the usage of models that could process text input and generate high fidelity images based on text descriptions. Text-to-image generation using neural networks could be traced back to the emergence of Generative Adversial Network (GAN), followed by the autoregressive Transformer. Diffusion models are one prominent type of generative model used for the generation of images through the systematic introduction of noises with repeating steps. As an effect of the impressive results of diffusion models on image synthesis, it has been cemented as the major image decoder used by text-to-image models and brought text-to-image generation to the forefront of machine-learning (ML) research. In the era of large models, scaling up model size and the integration with large language models have further improved the performance of TTI models, resulting the generation result nearly indistinguishable from real-world images, revolutionizing the way we retrieval images. Our explorative study has incentivised us to think that there are further ways of scaling text-to-image models with the combination of innovative model architectures and prediction enhancement techniques. We have divided the work of this survey into five main sections wherein we detail the frameworks of major literature in order to delve into the different types of text-to-image generation methods. Following this we provide a detailed comparison and critique of these methods and offer possible pathways of improvement for future work. In the future work, we argue that TTI development could yield impressive productivity improvements for creation, particularly in the context of the AIGC era, and could be extended to more complex tasks such as video generation and 3D generation