Performance profiling
If you like applying Performance profiling, every challenge here gives you a chance to practice it on a real industry brief.
- AnalysisSeniorNew
Amortized-Analysis Investigation of a Production Telemetry Cache
Working only from the three provided materials — the de-identified incident record (incident-record), the representative cache source module (cache-source), and the recorded ope…
- Amortized Analysis
- Data Structures
- Algorithm Analysis
Open coursework - AnalysisIntermediateNew
Diagnose a Memory-Hierarchy Bottleneck in a Trading-System Hot Path
Receive the normalizer source (around 4,000 lines of C++17), a replay harness that feeds 30 minutes of recorded market data, and host-machine specifications (Intel Xeon Gold 634…
- Memory Hierarchy
- Performance Profiling
- Perf
Open coursework - AnalysisBeginnerNew
Profile and Optimize a Virtual-Memory-Heavy Image Pipeline
Receive the Go pipeline source, a representative batch (1,200 photos averaging 12MB each, with 30 outliers over 80MB), and host specs (4-core, 16GB RAM, Linux kernel 5.15). Run …
- Virtual Memory
- Performance Profiling
- Memory Hierarchy
Open coursework - CodeIntermediateNew
Scale Feature Pipelines for a Hyperscaler Search-Ranking Team
You receive a synthetic-but-realistic 80 GB sample of the ranking events plus the existing Spark pipeline (PySpark) and a Spark UI snapshot from a recent production run. Profile…
- Spark
- Distributed Systems
- Performance Profiling
Open coursework Practice your coursework on real scenarios.
Every challenge is shaped from real-world context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- CodeBeginnerNew
Build an I/O Benchmarking Harness for an Edge Storage Appliance
Receive the appliance specs (4x 7.68TB Gen4 NVMe, ZFS, Linux kernel 5.15), the 3 target workload profiles (4KB random read at QD32, 1MB sequential write at QD8, mixed 70/30 read…
- Io Benchmarking
- Fio
- System Calls
Computer Systems and Organization - AnalysisIntermediateNew
Cost-Optimize a Large-Scale Spark Job for an Ad-Tech Platform
You receive the Spark job source (PySpark), the EMR cluster config, and 5 nights of job-history JSON. Profile the job with the Spark UI + EMR metrics, identify the top 3 cost dr…
- Spark Optimization
- Cloud Services
- Cost Engineering
Cloud Computing for Data and ML - AnalysisSeniorNew
Tame the P99 Latency Tail of a Real-Time Ad-Auction Service
Working from the four provided materials only, isolate and fix the latency tail of the bidder. Start with the representative auction-handler module ('bidder-module') as the code…
- Performance Optimization
- Ebpf
- Go
Open coursework
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
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