block_size>>>(N, x, y); // … } Ok, about the kernel parameters 10 |Memory“In a typical PC or cluster node today, the memories of the CPU and GPU are physically distinct and separated by the PCI-Express Memory GPU Memory 12 |“Unified Memory creates a pool of managed memory that is shared between the CPU and GPU, bridging the CPU-GPU divide. Managed memory is accessible to both the CPU and GPU using a y[i] += f(i); } 23 |“Unified Memory creates a pool of managed memory that is shared between the CPU and GPU, bridging the CPU-GPU divide. Managed memory is accessible to both the CPU and GPU using a

kubernetes cluster in large scale Patrickxie ( 谢谆志） Background Cloud has been the general trend. How to manage so many clusters ,resources and businesses How to ensure load balancing of cluster nodes resources and businesses Cluster1 Cluster2 Cluster2 … Region N … Cluster1 Cluster2 … Cluster2 Business 1 Business 2 Business 3 Business N … How to ensure load balancing of cluster nodes ？ Dynamic-Scheduler Controller of native K8S use only one goroutine to handle the scaling of all businesses in the cluster, and personalization configuration is not supported. Worker HPA1 HPA2 … HPAn HPA1 HPAPlus-controller

ClickHouseInstallation YAML file Лицензия: Apache 2.0, Распространяется как Docker image ClickHouse cluster resources kubectl apply K8S API Спецификация ClickHouse on Kubernetes – components Shard 1 Replica com/Altinity/clickhouse-operator/master/manifests/oper ator/clickhouse-operator-install.yaml Single-node “cluster” apiVersion: "clickhouse.altinity.com/v1" kind: "ClickHouseInstallation" metadata: name: "demo-01" STATUS pod/chi-demo-01-demo-0-0-0 1/1 Running NAME TYPE CLUSTER-IP service/chi-demo-01-demo-0-0 ClusterIP None service/clickhouse-demo-01 LoadBalancer

tlsCertificate.certificateChain.i nlineBytes' -r | base64 -d | openssl x509 -noout -text -in - ● Part of cluster config in envoy config-dump ○ kubectl exec -c istio-proxy curl localhost:15000/config_dump selector: matchLabels: app: details rules: - from: - source: principals: ["cluster.local/ns/default/sa/bookinfo-productpage"] to: - operation: methods: ["GET"]" apiVersion: selector: matchLabels: app: reviews rules: - from: - source: principals: ["cluster.local/ns/default/sa/bookinfo-productpage"] to: - operation: methods: ["GET"]" apiVersion:

pods via VIP using a load balancer • Two types • ClusterIP provides in-cluster access • NodePort provides out-of-cluster access • Major modes • Iptables • IPVS Iptables mode • How it works configure may differ • Performance of a cluster in different time slot may differ • Due to CPU oversold • Suggestion: • Run the test against the same cluster during near time • Make CPU the bottleneck

cudaDeviceSynchronize() 实 际上可以删掉了。 统一内存地址技术（ Unified Memory ） • 还有一种在比较新的显卡上支持的特性， 那就是统一内存 (managed) ，只需把 cudaMalloc 换成 cudaMallocManaged 即可，释放时也是通过 cudaFree 。这样 分配出来的地址，不论在 CPU 还是 GPU 上都是一模一样的，都可以访问。而 些数据结构。 注意不要混淆 • 主机内存 (host) ： malloc 、 free • 设备内存 (device) ： cudaMalloc 、 cudaFree • 统一内存 (managed) ： cudaMallocManaged 、 cudaFree • 如果我没记错的话，统一内存是从 Pascal 架构开始支持的，也就是 GTX9 开头及以上 。 • 虽然方便，但并非完 数 据排列在内存中，而 arr 则是指向其起始 地址。然后把 arr 指针传入 kernel ，即 可在里面用 arr[i] 访问他的第 i 个元素。 • 然后因为我们用的统一内存 (managed) ， 所以同步以后 CPU 也可以直接读取。 多个线程，并行地给数组赋值 • 刚刚的 for 循环是串行的，我们可以把线 程数量调为 n ，然后用 threadIdx.x 作为 i

control_block::decrement_ref_count() { if (ref_count.fetch_sub(1) == 1) { dispose(); // Delete the managed object delete this; // Delete the control block } } Intentionally ignoring the weak