performance • Easiser operation • Cloud native • Curve is composed of two parts • Curve Block Storage (CurveBS) • CurveBS: a high performance cloud native distributed block storage • Curve File File System (CurveFS) • CurveFS: a high performance cloud native file system (Based on CurveBS / S3 compatible Storage)Operator capability Level OPERATOR CAPABILITY LEVEL CURVE COMMENT BASIC INSTALL Y Support horizontal/vertical scaling, auto config tuning, abnormal detection, schedule tuningCloud native Feature list • Features for Cluster • Features for CurveBS • Features for CurveFS • Features

simulate the workload. Embra can simulate real workloads at speeds only 3 to 9 times slower than native execution of the workload. Can customize its generated code to include a processor cache model slowdowns of only a factor of 7 to 20. — 1996 (read 3 paragraphs) Simulation is the process of running native executable machine code for one architecture on another architecture. Dynamic binary translation: simulate the workload. Embra can simulate real workloads at speeds only 3 to 9 times slower than native execution of the workload. Can customize its generated code to include a processor cache model

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1840 105.7 High-level Overview of the Native-Code Generation Process . . . . . . . . . . . . . . 1841 105.8 Julia Functions . . . . . . . . Arithmetic, which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's Julia also defines the types Int and UInt, which are aliases for the system's signed and unsigned native integer types respectively: # 32-bit system: julia> Int Int32CHAPTER 5. INTEGERS AND FLOATING-POINT

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1840 105.7 High-level Overview of the Native-Code Generation Process . . . . . . . . . . . . . . 1841 105.8 Julia Functions . . . . . . . . Arithmetic, which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's Julia also defines the types Int and UInt, which are aliases for the system's signed and unsigned native integer types respectively: # 32-bit system: julia> Int Int32CHAPTER 5. INTEGERS AND FLOATING-POINT

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1840 105.7 High-level Overview of the Native-Code Generation Process . . . . . . . . . . . . . . 1841 105.8 Julia Functions . . . . . . . . Arithmetic, which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's Julia also defines the types Int and UInt, which are aliases for the system's signed and unsigned native integer types respectively: # 32-bit system: julia> Int Int32CHAPTER 5. INTEGERS AND FLOATING-POINT

2022]CPU PYTHON NATIVE SYS% AVERAGE & PEAK COPY VOLUME OVER TIME, % OF MEM ALLOCATED MEMORY USAGE GPU UTIL %, PEAK MEMORY (MB/s) MEMORY PYTHON NATIVECPU PYTHON NATIVE SYS% AVERAGE & ALLOCATED MEMORY USAGE GPU UTIL %, PEAK MEMORY (MB/s) MEMORY PYTHON NATIVECPU PYTHON NATIVE SYS% AVERAGE & PEAK COPY VOLUME OVER TIME, % OF MEM ALLOCATED MEMORY USAGE GPU UTIL % optimize your code!CPU PYTHON NATIVE SYS% AVERAGE & PEAK COPY VOLUME OVER TIME, % OF MEM ALLOCATED MEMORY USAGE GPU UTIL %, PEAK MEMORY (MB/s) MEMORY PYTHON NATIVE AI-powered optimizations

Curve High performance Cloud native Distributed storage system https://www.opencurve.io/Agenda • What is Curve • Use Cases • CurveBS • Key Features • Comparing to Ceph • CurveFS • Comparing (CurveBS) • CurveBS: a high performance cloud native distributed block storage • Curve File System (CurveFS) • CurveFS: a high performance cloud native file systemUse Cases • Container • Database Glacier, aliyun EBS, aliyun OSS) or on-prem (baremetal, HDFS, OSS) and turns it into container-native storageDatabase • Database services orchestrated in the cloud • Curve can backup / sync data

http_client.fetch(url) return response.body And here is the same function rewritten asynchronously as a native coroutine: from tornado.httpclient import AsyncHTTPClient async def asynchronous_fetch(url): http_client 15Tornado Documentation, Release 6.5.1 Native vs decorated coroutines Python 3.5 introduced the async and await keywords (functions using these keywords are also called “native corou- tines”). For compatibility you can use “decorated” or “yield-based” coroutines using the tornado.gen.coroutine decorator. Native coroutines are the recommended form whenever possible. Only use decorated coroutines when compatibility

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1893 106.7 High-level Overview of the Native-Code Generation Process . . . . . . . . . . . . . . 1894 106.8 Julia Functions . . . . . . . . Arithmetic, which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's Julia also defines the types Int and UInt, which are aliases for the system's signed and unsigned native integer types respectively: # 32-bit system: julia> Int Int32CHAPTER 5. INTEGERS AND FLOATING-POINT

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1891 106.7 High-level Overview of the Native-Code Generation Process . . . . . . . . . . . . . . 1892 106.8 Julia Functions . . . . . . . . Arithmetic, which can handle operations on numeric values that cannot be represented effectively in native hardware representations, but at the cost of relatively slower performance. The following are Julia's Julia also defines the types Int and UInt, which are aliases for the system's signed and unsigned native integer types respectively: # 32-bit system: julia> Int Int32CHAPTER 5. INTEGERS AND FLOATING-POINT