推動研究前進的計算
- 雲端
- 站在雲端
- 隨需
- 根據需要擴大規模
- 代管式
- 包括操作
- 已調校
- 特定於求解器的設置
核心能力
從設計到操作都在一個軟體包中 - 因此研究人員可以專注於工作，而不是基礎設施。
託管雲端 HPC
在雲端設計和建置的專用叢集。無需資料中心，無需內部管理團隊。
每個解算器調整
針對 DFT、MD 和其他工作負載進行了調整的編譯器、函式庫和 MPI 設定 — 在相同的硬體上更快地獲得結果。
突發容量
當內部 HPC 已滿或隊列很長時，請立即啟動額外的節點，這樣活動就不會停滯。
作業排程程式
基於標準 Slurm 的調度，具有優先順序、配額和公平共享策略，用於團隊級資源共享。
監控與營運
利用率、佇列狀態和作業歷史記錄的即時檢視 - 事件回應由 VirtualLab 營運團隊處理。
為你運行的內容付費
雲端計費意味著您按實際使用的時間付費，帳面上沒有閒置的硬體。
Getting results sooner from the same hardware
Cloud HPC performance depends far more on how the solver is built and how it communicates on those nodes than on how many nodes you switch on.
What tuning per solver actually means
The same source, built with a different compiler, linked against a different numerical library, running on a different MPI implementation, produces visibly different wall-clock times for an identical calculation. DFT codes are sensitive to FFT and linear algebra performance; MD codes to neighbour-list construction and inter-node latency. MAXFlops ships images with that combination already matched to the solvers your team actually runs, so nobody has to experiment with build flags.
How many nodes to attach
Parallel calculations do not run twice as fast on twice the nodes. Past some point communication cost overtakes the compute gain, and where that point sits depends on system size and solver. During onboarding we measure scaling on a representative calculation and set a sensible node count per job from it — over-parallelising costs more and returns results later.
Scheduling and sharing
A standard Slurm scheduler means existing HPC experience transfers unchanged. Priorities and quotas per team or project, plus a fair-share policy, stop one person's large campaign from blocking everyone else's queue. Utilisation, queue state and job history are visible in real time.
Data and cost
Results outlive the nodes that produced them, so storage and compute are designed with separate lifetimes. Resources stay up only for the hours they are needed, following cloud billing rather than leaving you holding idle hardware. Sizing starts from the solvers you run, concurrent users, data volume and budget, worked through together.
Why your existing workflow survives
Because the scheduler is standard Slurm, the submission scripts and habits from an in-house cluster mostly carry over unchanged. That was the point: learning a new command set or rewriting a pipeline should not be what stops a team from adding capacity. Where finished results are retrieved to, and how, is settled during onboarding — after which the aim is that a researcher cannot tell whether a job ran locally or in the cloud.
如何交付
我們傾聽您的工作負載，為其設計一個集群，然後啟動它。從那裡開始，你就可以使用它——這就是整個流程。
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定義工作負載
您主要運行的解決方案、並髮用戶、資料大小和預算 - 我們一起解決這些問題。
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設計與站立
實例、網路、儲存和軟體堆疊在雲端設計，針對您的求解器進行調整，並上線。
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運行和操作
研究人員只需提交工作即可。監控、事件回應和橫向擴展由 VirtualLab 處理。
團隊在哪裡使用它
大規模物資宣傳活動
對許多候選結構和數千個作業資料產生管道進行 DFT/MD 篩選。
AI訓練資料生成
材料 ML 的計算資料集 — 必須在截止日期內完成的工作負載。
日常 DFT 和 MD
將因內部HPC排隊而停滯的日常運算，依需求轉移到雲端。
短期合作和項目
在專案期間啟動專用資源，然後拆除－無採購週期。
MAXFlops — frequently asked questions
What is MAXFlops?
A managed service that designs, builds and operates a dedicated HPC cluster for your team on the cloud. You get a large-scale compute environment without procurement, a data centre, or a dedicated administrator.
We already have in-house HPC. Why would we need this?
As headroom. When in-house resources run short or queues grow long, extra nodes come online immediately so a calculation campaign does not stall — expanding for the period you need instead of permanently.
How are jobs submitted and resources shared?
Through a standard Slurm scheduler, with per-team priority, quota and fair-share policies. Utilisation, queue state and job history are visible in real time.
What does "tuned per solver" mean?
Compiler options, numerical libraries and inter-node communication are optimised for the solvers you actually run — DFT, MD and others. The same resources return results sooner, and researchers never have to tune the environment themselves.
How is the cost determined?
Cloud billing for the hours the resources are actually up. We size the cluster after going through the solvers you run, concurrent users, data volume and budget together.