About Link trace
Home / J-Link/J-Trace User's GuideWith Segger's J-Link/J-Trace debugger adapters and the OEM Versions you can debug and traceapplications on ARM7, ARM9, and Cortex-M processor-based devices.µVision runs with all J-Link/J-Trace adapters that are notIDE-dependent.Minimum requirements to use J-Link/J-Trace are: Keil MDK-ARM, version 4.10 or higher. Segger J-Link driver for Windows.Common features of J-Link/J-Trace are: Examine memory and registers. Programming Flash Memory. Debugging in JTAG and Serial Wire mode. Single-stepping through programs and inserting multiple breakpoints. Supporting Segger's Flash Breakpoint feature, which allows more breakpoints than the hardware can actually handle. Adapter specific features are listed below: J-Link is a JTAG emulator that connects to the ARM standard JTAG 20-pin connector. J-Link Pro is fully compatible to J-Link, and adds Ethernet connection possibilities. J-Trace Cortex–M is fully compatible to J-Link, supports debugging and tracing on Cortex-M processor-based devices, and debugging on ARM7 and ARM9 processor-based devices. This adapter connects to the Cortex Debug+ETM 20-pin connector. J-Trace is an emulator that supports the Embedded Trace Macrocell (ETM) for ARM7 and ARM9 processor-based devices. J-Link J-Link Pro J-Trace Cortex-M J-Trace USB yes yes yes yes Ethernet no yes no no Supported Cores Cortex-M ARM7, ARM9 Cortex-M ARM7, ARM9 Cortex-M ARM7, ARM9 - no tracing ARM7, ARM9 JTAG yes yes yes yes SWD yes yes yes no SWO yes yes yes no ETM Trace no no yes yes
Key features of the Link trace that are enabled through these drivers include:
- Traces: Link a trace to multiple sessions langfuse - GitHub
- Link Redirect Trace เครื่องมือตรวจสอบการเปลี่ยนเส้นทาง
- - Segger - Debugger, J-Link Trace PRO Cortex, Trace
- SEGGER, Debugger, J-Link Trace PRO Cortex, Trace
- Link Redirect Trace chrome extension
- Glova Link Tracking – Track and Trace
- Link Redirect Trace Archives - Linktracker
Alibaba Cluster Trace ProgramOverviewThe Alibaba Cluster Trace Program is published by Alibaba Group. By providing cluster trace from real production, the program helps the researchers, students and people who are interested in the field to get better understanding of the characterastics of modern internet data centers (IDC's) and the workloads.So far, four versions of traces have been released:cluster-trace-v2017 includes about 1300 machines in a period of 12 hours. The trace-v2017 firstly introduces the collocation of online services (aka long running applications) and batch workloads. To see more about this trace, see related documents (trace_2017). Download link is available after a short survey (survey link).cluster-trace-v2018 includes about 4000 machines in a period of 8 days. Besides having larger scaler than trace-v2017, this piece trace also contains the DAG information of our production batch workloads. See related documents for more details (trace_2018). Download link is available after a survey (less than a minute, survey link).cluster-trace-gpu-v2020 includes over 6500 GPUs (on ~1800 machines) in a period of 2 months. It describes the AI/ML workloads in the MLaaS (Machine-Learning-as-a-Service) provided by the Alibaba PAI (Platform for Artificial Intelligence) on GPU clusters. See the subdirectory (pai_gpu_trace_2020) for the released data, schema, and scripts for processing and visualization. Our analysis paper published in USENIX NSDI '22 is available here.cluster-trace-microservices-v2021 contains 20000+ microservices in a period of 12 hours. The traces the first released to introduce the runtime metrics of microservices in the production cluster, including call dependencies, respond time, call rates, and so on. See the subdirectory (trace_2021) for more details. Our analysis paper, accepted by SoCC '21, is available here.cluster-trace-microarchitecture-v2022 first provides AMTrace (Alibaba Microarchitecutre Trace). AMTrace is the first fine-granulairty and large-scale microarchitectural metrics of Alibaba Colocation Datacenter. Based AMTrace, researchers can analysis: CPU performance, microarchitecture contention, memory bandwidth contention and so on. Our paper is accepted by ICPP'22. See the subdirectory (trace_2022) for more details.cluster-trace-gpu-v2023 includes over 6200 GPUs (on ~1200 machines). It describes the AI/ML workloads with diverse resource specifications in a heterogeneous GPU cluster. In our "Beware of Fragmentation" paper (published in USENIX ATC '23), we modeled this trace in a
