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6 days ago · Run frontend and backend services, batch jobs, host LLMs, and queue processing workloads without the need to manage infrastructure. Cloud Run gives you on-demand GPU access …
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Run frontend and backend services, batch jobs, host LLMs, and queue processing workloads without the need to manage infrastructure. Cloud Run gives you on-demand GPU access for hosting LLMs …
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6 days ago · Run frontend and backend services, batch jobs, host LLMs, and queue processing workloads without the need to manage infrastructure. Cloud Run gives you on-demand GPU access …
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Run frontend and backend services, batch jobs, host LLMs, and queue processing workloads without the need to manage infrastructure. Cloud Run gives you on-demand GPU access for hosting LLMs …
arxiv.org/abs/2104.01744v2
UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO can consider a variety of tuning choices, reaching from picking transaction code variants over index selections up to database system parameter tuning. UDO use...
www.bing.com/ck/a?!&&p=d203ac1577ccb0cf14f449b51ce0cbae53dbe9a12a55d728e5b8ad824bfe5fafJmltdHM9MTc3Mjc1NTIwMA&ptn=3&ver=2&hsh=4&fclid=265602e4-47b2-6456-2574-15f146ab65d7&u=a1aHR0cHM6Ly9zdXBlcnVzZXIuY29tL3F1ZXN0aW9ucy8xODE2NTQ2L2hvdy1hbmQtd2h5LWRvZXMtdGhlLWRldi1kcml2ZS1mZWF0dXJlLW9mLXdpbmRvd3MtMTEtd29yaw&ntb=1
Windows 11 has a “dev drive” feature which can improve performance of developer workloads. Developer drives use ReFS. Questions: Why is using ReFS going to help performance? After all, in …
arxiv.org/abs/2510.09932v1
Tensor compilers play a key role in enabling high-performance implementations of deep learning workloads. These compilers rely on existing CPU and GPU code generation backends to generate device-specific code. Recently, many tensor accelerators (neur...
arxiv.org/abs/2003.10950v2
The data center of tomorrow is a data center made up of heterogeneous systems, which will run heterogeneous workloads. The systems will be located as close as possible to the data. Heterogeneous systems will be equipped with binary, biological inspir...
arxiv.org/abs/1810.09868v1
Google's Cloud TPUs are a promising new hardware architecture for machine learning workloads. They have powered many of Google's milestone machine learning achievements in recent years. Google has now made TPUs available for general use on their clou...
arxiv.org/abs/2501.00298v1
Supervised machine learning techniques have shown promising results in code analysis and optimization problems. However, a learning-based solution can be brittle because minor changes in hardware or application workloads -- such as facing a new CPU a...
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Windows 11 has a “dev drive” feature which can improve performance of developer workloads. Developer drives use ReFS. Questions: Why is using ReFS going to help performance? After all, in …
arxiv.org/abs/2108.10540v1
Joins in native graph database management systems (GDBMSs) are predefined to the system as edges, which are indexed in adjacency list indices and serve as pointers. This contrasts with and can be more performant than value-based joins in RDBMSs and h...
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Feb 24, 2026 · We’re announcing a long-term agreement with AMD to power our AI infrastructure with up to 6GW of AMD Instinct GPUs, helping us build a flexible, resilient tech stack for our AI workloads.
github.com/MicrosoftResearch/Naiad
The Naiad system provides fast incremental and iterative computation for data-parallel workloads (⭐ 527)
arxiv.org/abs/2509.21039v1
We explore the performance and portability of the novel Mojo language for scientific computing workloads on GPUs. As the first language based on the LLVM's Multi-Level Intermediate Representation (MLIR) compiler infrastructure, Mojo aims to close per...
arxiv.org/abs/1202.5358v1
Differential privacy is a strong notion for protecting individual privacy in privacy preserving data analysis or publishing. In this paper, we study the problem of differentially private histogram release for random workloads. We study two multidimen...
github.com/skypilot-org/skypilot
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 20+ clouds, or on-prem). (⭐ 9542)
arxiv.org/abs/2508.03900v1
The fast evolution of Machine Learning (ML) models requires flexible and efficient hardware solutions as hardwired accelerators face rapid obsolescence. Vector processors are fully programmable and achieve high energy efficiencies by exploiting data...
arxiv.org/abs/1711.05852v1
Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve big models with numerous parameters. Once trained, a chal...
github.com/rancher/fleet
Deploy workloads from Git to large fleets of Kubernetes clusters (⭐ 1688)
