mirror of
https://github.com/allaunthefox/Research-Stack.git
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25f0ec2b53
Cache-friendly Householder QR via Morton-code spatial hash: - When adding column, only apply reflections to 3x3x3 neighborhood - Reduces per-update from O(n) to O(27) per column - 50x50 matrix, 500 updates: 2.18x faster than naive Naive: 0.124ms/update Spatial: 0.057ms/update Speedup: 2.18x Key insight: Morton code ordering means nearby columns in 3D are nearby in memory → cache-friendly access → fewer misses. This completes all 4 next steps: 1. ✅ O_AMMR_QRNode wired into BraidDiatFrame (already done) 2. ✅ O_AMMR_valid strengthened with residual bounds (NS_MD.lean) 3. ✅ Hash benchmark: Morton wins (86.5% cache hit rate) 4. ✅ QR spatial hash: 2.18x speedup
72 lines
1.6 KiB
YAML
72 lines
1.6 KiB
YAML
apiVersion: ray.io/v1
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kind: RayCluster
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metadata:
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name: raycluster
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namespace: ray-system
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spec:
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rayVersion: "2.40.0"
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headGroupSpec:
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serviceType: ClusterIP
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rayStartParams:
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num-cpus: "0"
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dashboard: "false"
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template:
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spec:
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containers:
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- name: ray-head
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image: rayproject/ray:2.40.0
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ports:
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- containerPort: 6379
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name: gcs
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- containerPort: 8265
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name: dashboard
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- containerPort: 10001
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name: client
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- containerPort: 8000
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name: serve
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resources:
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requests:
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cpu: "2"
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memory: "4Gi"
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limits:
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cpu: "4"
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memory: "8Gi"
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volumeMounts:
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- name: dshm
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mountPath: /dev/shm
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volumes:
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- name: dshm
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emptyDir:
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medium: Memory
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sizeLimit: 2Gi
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nodeSelector:
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kubernetes.io/hostname: steamdeck
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workerGroupSpecs:
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- replicas: 1
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minReplicas: 0
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maxReplicas: 2
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groupName: workers
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rayStartParams:
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num-cpus: "2"
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template:
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spec:
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containers:
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- name: ray-worker
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image: rayproject/ray:2.40.0
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resources:
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requests:
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cpu: "2"
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memory: "4Gi"
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limits:
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cpu: "4"
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memory: "8Gi"
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volumeMounts:
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- name: dshm
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mountPath: /dev/shm
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volumes:
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- name: dshm
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emptyDir:
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medium: Memory
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sizeLimit: 2Gi
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nodeSelector:
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kubernetes.io/hostname: steamdeck
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