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Current status, needs, and challenges in Heterogeneous and Composable Memory from the HCM workshop (HPCA’23)

ACM Sigarch

Heterogeneous and Composable Memory (HCM) offers a feasible solution for terabyte- or petabyte-scale systems, addressing the performance and efficiency demands of emerging big-data applications. Such a combination requires new abstractions and programming models for effective management. The recently announced CXL3.0

Latency 52
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USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon

Brendan Gregg

My personal opinion is that I don't see a widespread need for more capacity given horizontal scaling and servers that can already exceed 1 Tbyte of DRAM; bandwidth is also helpful, but I'd be concerned about the increased latency for adding a hop to more memory. The call for participation ends on March 2nd 23:59 SGT! Ford, et al., “TCP

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USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon

Brendan Gregg

My personal opinion is that I don't see a widespread need for more capacity given horizontal scaling and servers that can already exceed 1 Tbyte of DRAM; bandwidth is also helpful, but I'd be concerned about the increased latency for adding a hop to more memory. It was a great privilege. That's about 24 hours from now!

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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

Traditional computing models rely on virtual or physical machines, where each instance includes a complete operating system, CPU cycles, and memory. VMware commercialized the idea of virtual machines, and cloud providers embraced the same concept with services like Amazon EC2, Google Compute, and Azure virtual machines.

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bpftrace (DTrace 2.0) for Linux 2018

Brendan Gregg

more capable, and built from the ground up for the modern era of the eBPF virtual machine. Screenshot: tracing read latency for PID 181: # bpftrace -e 'kprobe:vfs_read /pid == 30153/ { @start[tid] = nsecs; } kretprobe:vfs_read /@start[tid]/ { @ns = hist(nsecs - @start[tid]); delete(@start[tid]); }'. eBPF does more. I wrote seeksize.d

C++ 110
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The Need for Real-Time Device Tracking

ScaleOut Software

Since big data analyses can take minutes or hours to run, they are typically used to look for big trends, like the fuel efficiency and on-time delivery rate of a trucking fleet, instead of emerging issues that need immediate attention. These limitations create an opportunity for real-time device tracking to fill the gap.

IoT 78
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The Power of Integrated Analytics Within an IMDG

ScaleOut Software

By transparently distributing stored objects across a cluster of servers (physical or virtual), it automatically scales performance for fast-growing workloads and maintains consistently low access latency. They also can perform analysis quickly and efficiently — where the data lives.