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Kernel and Graphics: Vulkan, NVIDIA Memory Compaction and Intel DRM Driver

Filed under
Graphics/Benchmarks
Linux
  • vkBasalt CAS Vulkan Layer Adds FXAA Support

    The open-source vkBasalt project is the independent effort implementing AMD Radeon Image Sharpening / Contrast Adaptive Sharpening technique as a Vulkan post-processing layer that can be used regardless of the (Vulkan-powered) game. With vkBasalt 0.1 also now comes the ability to apply FXAA.

    Fast Approximate Anti-Aliasing (FXAA) is the latest feature of vkBasalt besides the contrast adaptive sharpening. However, for the v0.1 release, CAS and FXAA cannot both be enabled at the same time. It's on the project TODO list for being able to enable both FXAA and CAS in a future release. Like the existing CAS support, the anti-aliasing technique can be used for any Vulkan game thanks to this being implemented as a post-processing layer for this graphics API.

  • mm: Proactive compaction
    For some applications we need to allocate almost all memory as
    hugepages. However, on a running system, higher order allocations can
    fail if the memory is fragmented. Linux kernel currently does on-demand
    compaction as we request more hugepages but this style of compaction
    incurs very high latency. Experiments with one-time full memory
    compaction (followed by hugepage allocations) shows that kernel is able
    to restore a highly fragmented memory state to a fairly compacted memory
    state within <1 sec for a 32G system. Such data suggests that a more
    proactive compaction can help us allocate a large fraction of memory as
    hugepages keeping allocation latencies low.
    
    For a more proactive compaction, the approach taken here is to define
    per page-node tunable called ‘hpage_compaction_effort’ which dictates
    bounds for external fragmentation for HPAGE_PMD_ORDER pages which
    kcompactd should try to maintain.
    
    The tunable is exposed through sysfs:
      /sys/kernel/mm/compaction/node-n/hpage_compaction_effort
    
    The value of this tunable is used to determine low and high thresholds
    for external fragmentation wrt HPAGE_PMD_ORDER order.
    
    Note that previous version of this patch [1] was found to introduce too
    many tunables (per-order, extfrag_{low, high}) but this one reduces them
    to just (per-node, hpage_compaction_effort). Also, the new tunable is an
    opaque value instead of asking for specific bounds of “external
    fragmentation” which would have been difficult to estimate. The internal
    interpretation of this opaque value allows for future fine-tuning.
    
    Currently, we use a simple translation from this tunable to [low, high]
    extfrag thresholds (low=100-hpage_compaction_effort, high=low+10%). To
    periodically check per-node extfrag status, we reuse per-node kcompactd
    threads which are woken up every few milliseconds to check the same. If
    any zone on its corresponding node has extfrag above the high threshold
    for the HPAGE_PMD_ORDER order, the thread starts compaction in
    background till all zones are below the low extfrag level for this
    order. By default. By default, the tunable is set to 0 (=> low=100%,
    high=100%).
    
    This patch is largely based on ideas from Michal Hocko posted here:
    https://lore.kernel.org/linux-mm/20161230131412.GI13301@dhcp22.suse.cz/
    
    * Performance data
    
    System: x64_64, 32G RAM, 12-cores.
    
    I made a small driver that allocates as many hugepages as possible and
    measures allocation latency:
    
    The driver first tries to allocate hugepage using GFP_TRANSHUGE_LIGHT
    and if that fails, tries to allocate with `GFP_TRANSHUGE |
    __GFP_RETRY_MAYFAIL`. The drives stops when both methods fail for a
    hugepage allocation.
    
    Before starting the driver, the system was fragmented from a userspace
    program that allocates all memory and then for each 2M aligned section,
    frees 3/4 of base pages using munmap. The workload is mainly anonymous
    userspace pages which are easy to move around. I intentionally avoided
    unmovable pages in this test to see how much latency we incur just by
    hitting the slow path for most allocations.
    
  • NVIDIA Engineer Continues Working On Proactive Memory Compaction For Linux

    NVIDIA's Nitin Gupta continues working on proactive compaction for the Linux kernel's memory management code.

    This proactive compaction is designed to avoid the high latency introduced right now when the Linux kernel does on-demand compaction when an application needs a lot of hugepages. With this proactive compaction, a large number of hugepages can be requested while avoiding high latencies.

  • Intel Submits Last Bits For Linux 5.5 DRM Driver - Includes More TGL/Gen12, Discrete Bit

    Intel's open-source crew has submitted the last of their feature updates to their "i915" Direct Rendering Manager graphics driver for staging in DRM-Next ahead of the upcoming Linux 5.5 kernel cycle.

    In the previous weeks they've been bringing up a lot of their Tiger Lake / Gen12 graphics code as the dominating theme for the Linux 5.5 kernel. There has also been Jasper Lake support, Xe multi-GPU prepping, and their other routine code clean-ups and driver improvements. Out this morning is the last of their feature work targeting Linux 5.5.

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Bringing PostgreSQL to Government

  • Crunchy Data, ORock Technologies Form Open Source Cloud Partnership for Federal Clients

    Crunchy Data and ORock Technologies have partnered to offer a database-as-a-service platform by integrating the former's open source database with the latter's managed offering designed to support deployment of containers in multicloud or hybrid computing environments. The partnership aims to implement a PostgreSQL as a service within ORock's Secure Containers as a Service, which is certified for government use under the Federal Risk and Authorization Management Program, Crunchy Data said Tuesday.

  • Crunchy Data and ORock Technologies Partnership Brings Trusted Open Source Cloud Native PostgreSQL to Federal Government

    Crunchy Data and ORock Technologies, Inc. announced a partnership to bring Crunchy PostgreSQL for Kubernetes to ORock’s FedRAMP authorized container application Platform as a Service (PaaS) solution. Through this collaboration, Crunchy Data and ORock will offer PostgreSQL-as-a-Service within ORock’s Secure Containers as a Service with Red Hat OpenShift environment. The combined offering provides a fully managed Database as a Service (DBaaS) solution that enables the deployment of containerized PostgreSQL in hybrid and multi-cloud environments. Crunchy PostgreSQL for Kubernetes has achieved Red Hat OpenShift Operator Certification and provides Red Hat OpenShift users with the ability to provision trusted open source PostgreSQL clusters, elastic workloads, high availability, disaster recovery, and enterprise authentication systems. By integrating with the Red Hat OpenShift platform within ORock’s cloud environments, Crunchy PostgreSQL for Kubernetes leverages the ability of the Red Hat OpenShift Container Platform to unite developers and IT operations on a single FedRAMP-compliant platform to build, deploy, and manage applications consistently across hybrid cloud infrastructures.