Core Infrastructure
CoreAccelerate — HPC Performance Optimization
CoreAccelerate
Research computing centers and HPC users face three performance challenges: **Suboptimal Algorithms**: Scientific codes often evolve organically over years, accumulating inefficiencies. Algorithms that worked well on 32-core systems show poor scaling at 1000+ cores. Memory access patterns optimized for one architecture perform poorly on newer hardware. Most researchers lack deep HPC expertise to identify these bottlenecks. **Hardware Underutilization**: Modern HPC systems combine CPUs, GPUs, high-speed interconnects, and complex memory hierarchies. Achieving high performance requires matching workload characteristics to hardware capabilities—but most applications use default configurations that leave performance on the table. CPUs idle while GPUs saturate, or vice versa. Memory bandwidth goes unused because data locality is poor. **Scaling Inefficiency**: Adding more compute resources should proportionally reduce time-to-solution, but real-world scaling is typically far from ideal. Communication overhead dominates as node counts increase. Load imbalance leaves cores idle. Parallel algorithms hit fundamental bottlenecks that prevent further speedup.
Who This Is For
**Computational Scientists** whose research is limited by available compute time rather than scientific questions—if your job queue always has work waiting, you need better performance. **HPC Center Directors** responsible for maximizing return on multimillion-dollar hardware investments while serving demanding user communities. **Research Computing Leaders** supporting diverse workloads who need to make strategic hardware procurement decisions based on actual performance data. This is for organizations running scientific simulations, molecular dynamics, computational fluid dynamics, climate modeling, genomics pipelines, or machine learning training where faster compute directly enables better science.
What You Get
CoreAccelerate delivers comprehensive performance engineering for your critical HPC workloads. We profile your applications, identify bottlenecks, and implement optimizations that typically deliver 2-5x performance improvements—sometimes achieving 10x+ for severely suboptimal starting points. Your research teams get faster results from existing hardware, enabling larger simulations, finer resolution studies, or broader parameter sweeps within the same wall-clock time and budget. When planning hardware upgrades, our scaling analysis shows precisely which resources to expand for maximum impact.
How We Work
Key Deliverables
1
Comprehensive Performance Profiling
Deep analysis identifying where time and resources are spent:
2
Algorithm Optimization & Parallelization
Code-level improvements for computational efficiency:
3
Memory Hierarchy Optimization
Maximizing data access efficiency:
4
Parallel Scaling Analysis
Understanding performance across core counts:
5
Hardware Configuration Tuning
System-level optimization:
6
GPU Acceleration Assessment
Strategic GPU integration:
7
I/O Subsystem Optimization
Accelerating data-intensive workloads:
8
Compiler and Library Optimization
Software toolchain tuning:
9
Performance Monitoring Infrastructure
Ongoing performance tracking:
10
Knowledge Transfer & Best Practices
Ensuring sustainable performance: