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Efficient Memory-Protected Integration of Add-On Software Subsystems in Small Embedded Automotive Applications
Current innovations in the automotive industry
evolve mainly in the electronics and software domain. This leads
to an increasing integration of additional software subsystems
into already existing electronic control units (ECUs) to cope with
the raised amount and complexity of present ECUs in modern
high-end vehicles. This paper discusses different approaches
which are required to integrate such add-on software subsystems
in an isolated memory domain, and considers particularly the
special needs of small embedded systemsâincluding the limited
hardware support. Special focus is brought to the efficient detection
of malicious memory accesses, as well as the benefits of
a thereupon possible and adaptable failure-handling strategy.
All investigations are based on a developed memory-protection
framework which has been tailored to the special needs of a sample
vehicle dynamics control system. Its usage allows the combination
of. integrating additional subsystems without reducing the main
applicationâs availability
Formal Derivation of Concurrent Garbage Collectors
Concurrent garbage collectors are notoriously difficult to implement
correctly. Previous approaches to the issue of producing correct collectors
have mainly been based on posit-and-prove verification or on the application of
domain-specific templates and transformations. We show how to derive the upper
reaches of a family of concurrent garbage collectors by refinement from a
formal specification, emphasizing the application of domain-independent design
theories and transformations. A key contribution is an extension to the
classical lattice-theoretic fixpoint theorems to account for the dynamics of
concurrent mutation and collection.Comment: 38 pages, 21 figures. The short version of this paper appeared in the
Proceedings of MPC 201
Multidimensional Range Queries on Modern Hardware
Range queries over multidimensional data are an important part of database
workloads in many applications. Their execution may be accelerated by using
multidimensional index structures (MDIS), such as kd-trees or R-trees. As for
most index structures, the usefulness of this approach depends on the
selectivity of the queries, and common wisdom told that a simple scan beats
MDIS for queries accessing more than 15%-20% of a dataset. However, this wisdom
is largely based on evaluations that are almost two decades old, performed on
data being held on disks, applying IO-optimized data structures, and using
single-core systems. The question is whether this rule of thumb still holds
when multidimensional range queries (MDRQ) are performed on modern
architectures with large main memories holding all data, multi-core CPUs and
data-parallel instruction sets. In this paper, we study the question whether
and how much modern hardware influences the performance ratio between index
structures and scans for MDRQ. To this end, we conservatively adapted three
popular MDIS, namely the R*-tree, the kd-tree, and the VA-file, to exploit
features of modern servers and compared their performance to different flavors
of parallel scans using multiple (synthetic and real-world) analytical
workloads over multiple (synthetic and real-world) datasets of varying size,
dimensionality, and skew. We find that all approaches benefit considerably from
using main memory and parallelization, yet to varying degrees. Our evaluation
indicates that, on current machines, scanning should be favored over parallel
versions of classical MDIS even for very selective queries
Performance comparison of point and spatial access methods
In the past few years a large number of multidimensional point access methods, also called
multiattribute index structures, has been suggested, all of them claiming good performance. Since no
performance comparison of these structures under arbitrary (strongly correlated nonuniform, short
"ugly") data distributions and under various types of queries has been performed, database
researchers and designers were hesitant to use any of these new point access methods. As shown in
a recent paper, such point access methods are not only important in traditional database applications.
In new applications such as CAD/CIM and geographic or environmental information systems, access
methods for spatial objects are needed. As recently shown such access methods are based on point
access methods in terms of functionality and performance. Our performance comparison naturally
consists of two parts. In part I we w i l l compare multidimensional point access methods, whereas in
part I I spatial access methods for rectangles will be compared. In part I we present a survey and
classification of existing point access methods. Then we carefully select the following four methods
for implementation and performance comparison under seven different data files (distributions) and
various types of queries: the 2-level grid file, the BANG file, the hB-tree and a new scheme, called
the BUDDY hash tree. We were surprised to see one method to be the clear winner which was the
BUDDY hash tree. It exhibits an at least 20 % better average performance than its competitors and is
robust under ugly data and queries. In part I I we compare spatial access methods for rectangles.
After presenting a survey and classification of existing spatial access methods we carefully selected
the following four methods for implementation and performance comparison under six different data
files (distributions) and various types of queries: the R-tree, the BANG file, PLOP hashing and the
BUDDY hash tree. The result presented two winners: the BANG file and the BUDDY hash tree.
This comparison is a first step towards a standardized testbed or benchmark. We offer our data and
query files to each designer of a new point or spatial access method such that he can run his
implementation in our testbed
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